Patent Publication Number: US-2022230410-A1

Title: Object localization in video

Description:
CROSS-REFERENCE TO RELATED APPLICATIONS 
     This application claims the benefit of the U.S. Provisional Patent Application No. 63/138,867 filed Jan. 19, 2021, which is incorporated herein by reference in its entirety. 
    
    
     TECHNICAL FIELD 
     This disclosure application relates generally to surveillance cameras. 
     BACKGROUND 
     Many properties are equipped with monitoring systems that include sensors and connected system components. Some property monitoring systems include cameras. 
     SUMMARY 
     Techniques are described for object localization in video. 
     Many residents and homeowners equip their properties with monitoring systems to enhance the security, safety, or convenience of their properties. A property monitoring system can include cameras that can obtain visual images of scenes at the property. A camera can detect and localize objects within a field of view (FOV). 
     In home security and smart home applications, there are scenarios in which it is desirable to localize an object in the camera field of view or in an area of interest within the field of view. The object can be a doormat, a trashcan, a bench, etc. As an example, a touchless doorbell that includes a camera may have a region in a camera field of FOV that is monitored for human detection. If a human is detected standing in the region, the doorbell rings. To make the region well-defined, a doormat may be classified as the region of interest. The camera can localize the doormat in the camera FOV and monitor the doormat for a human standing event. When the camera detects that a person is standing on the doormat, the camera triggers the doorbell to ring. 
     The described techniques can be used to perform object localization without requiring a large corpus of data with annotations. Thus, object localization can be performed while reducing the amount of data and processing required. This can reduce the amount of data storage and power needed to perform object localization, and can also improve the speed of performing object localization. 
     The described techniques use a reference image of an object of interest to generate a model, or representation, of the object. The process for generating the object representation may also consider camera calibration parameters and camera FOV. The object representation can be generated using multiple homographic and photometric augmented images of the reference image. This approach provides a form of self-supervision which boosts the geometric and photometric consistency of interest points and their local descriptors. Aggregation of all these representations by mapping local descriptors to the reference image and fusing them will generate a robust model for the object. 
     The generated object representation can be used to localize the object in different conditions in FOV. For example, a camera can use the object representation to localize the object in various lighting conditions and weather conditions, and at various distances and orientations. 
     The details of one or more implementations of the subject matter described in this specification are set forth in the accompanying drawings and the description below. Other features, aspects, and advantages of the subject matter will become apparent from the description, the drawings, and the claims. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  illustrates an example system for object localization in video. 
         FIG. 2  illustrates an example system for generating a representation of an object using homographic adaptation. 
         FIG. 3  illustrates an example system for localizing an object in a sample image using the generated representation. 
         FIG. 4  is a flow diagram of an example process for homography estimation. 
         FIG. 5  is a flow diagram of an example process for object localization in video. 
         FIG. 6  is a diagram illustrating an example of a home monitoring system. 
     
    
    
     Like reference numbers and designations in the various drawings indicate like elements. 
     DETAILED DESCRIPTION 
       FIG. 1  illustrates an example system  100  for object localization in video. 
     The system  100  includes a camera  102  that captures video. The video includes multiple image frames captured over time. The camera  102  can perform video analysis on the video. Video analysis can include detecting, identifying, and tracking objects in the video. 
     The camera  102  can localize objects in the video. For example, the camera  102  can determine a shape, size, and orientation of an object in image frames of the video. The location of an object in an image frame can be described using pixel locations. For example, the image frame may include a grid of pixels having an x-axis and a y-axis. The location of each pixel may correspond to a respective x value and y value of the grid. The camera  102  can localize the object by determining locations of features of the object such as a center, edges, corners, etc. 
     The camera  102  includes a representation engine  108 . The representation engine  108  generates an object representation  120  from a reference image  110  using camera calibration and field of view (FOV)  111 . The representation engine  108  includes a homographic adaptor  112 , a photometric adaptor  114 , and an interest point extractor  116 . 
     The reference image is an image of an object that is to be localized by the camera  102 . For example, the reference image  110  includes an object  105  that is a doormat. The doormat includes text of the word “WELCOME.” The camera  102  generates a representation of the doormat in order to localize the doormat in video. When the location of another object overlaps with the location of the doormat, the camera  102  may perform an action. For example, when a package is left on the doormat, and the location of the package overlaps with the location of the doormat, the camera  102  may send an instruction to activate a doorbell chime. In another example, when a person steps on the doormat, the camera  102  may send an instruction to illuminate a porch light. 
     From the reference image  110 , the representation engine  108  can determine features of the object  105 . For example, the representation may determine a size and shape of the object  105 . Though the example object illustrated in  FIG. 1  is a quadrilateral shape, the process for generating an object representation  120  can be used for objects having other planar shapes. For example, the processes for localizing objects in video can be applied to an object having another polygonal shape such as a triangular, pentagonal, hexagonal, or octagonal shape. In some examples, the processes for localizing objects in video can be applied to an object having a non-polygonal shape such as a circular, semicircular, oblong, or elliptical shape. 
     The representation engine  108  receives camera calibration data and the camera FOV  111 . Camera calibration data can include intrinsic and extrinsic camera parameters. For example, camera calibration data can include camera height, focal length, imaging plane position, orientation, tilt, lens distortion, etc. The camera FOV can include an angular FOV of the camera, an optical axis of the camera  102 , and a range of the camera  102 . 
     The object representation  120  includes a robust local representation for the object  105 . The robust local representation includes a set of robust interest points that are selected based on aggregated probability and repeatability. A process for generating the object representation  120  is described in greater detail with reference to  FIG. 2 . 
     The camera  102  includes a localization engine  115  that determines an object location  140  in a sample image  130  based on the object representation  120 . The localization engine  115  includes interest point matcher  122 , a center estimator  124 , and a homography estimator  126 . 
     The sample image  130  may be an image captured by the camera  102 . For example, the sample image  130  may be an image of the object  105  positioned in an area that is monitored by the camera  102 . As an example, the sample image  130  may be an image of the doormat on a porch of a property. 
     The object location  140  can include pixel coordinates of features of the object  105 . For example, the object location  140  can include pixel coordinates of a center of the object  105 , pixel coordinates of corners of the object  105 , etc. A process for determining the object location  140  is described in greater detail with reference to  FIG. 3   
       FIG. 2  illustrates an example system  200  for generating a representation of an object using homographic adaptation. 
     The homographic adaptor  112  generates various homographic augmentations of the object  105 . For example, the homographic adaptor  112  can generate homographic adapted images  202 . The homographic adaptor  112  uses the calibration camera and FOV  111  to generate the homographic adapted images  202 . Based on the camera calibration parameters, the reference image of the object  105  is projected to the camera FOV. The projection process simulates an image recording using the camera  102 . For example, if the camera has lens distortion or if any de-warping algorithm is applied to the image before the image being recorded, those steps are considered in the homographic augmentation of the object  105 . By varying the parameters regarding camera height and tilt and object distance from the camera, multiple homographic adaptations of the object  105  are created. 
     The homographic adaptations simulate various scenarios of object  105  placement in the camera FOV. For example, the homographic adapted images can depict the object at various ranges to the camera, e.g., closer to the camera and further from the camera. The homographic adapted images can also depict the object in rotated or tilted positions. The homographic adapted images can also depict the object in various locations of the FOV, e.g., near a center of the FOV, to the right of center, to the left of center, near a corner of the FOV, etc. 
     The photometric adaptor  114  generates various photometric adaptations of the object  105 . For example, the photometric adaptor  114  can generate photometric adapted images  204  from the homographic adapted images  202 . The photometric adapted images  204  are generated by applying local and global illumination augmentation. Illumination augmentation can include random changes in the brightness or contrast of the image, adding shade, adding highlights, etc. 
     In some implementations, photometric adaptation may be performed before homographic adaptation. In some implementations, homographic adaptation may be performed, and photometric adaptation might not be performed. In some implementations, photometric adaptation may be performed, and homographic adaptation might not be performed. Once the adapted images are generated, local descriptors are then extracted from the homographic and photometric augmented versions of the object  105  and are projected back to the reference image. 
     The interest point extractor  116  extracts interest points  206  and corresponding descriptors from the photometric adapted images  204 . An interest point is a distinctive point that can be mapped between images. An interest point can be, for example, a point in an image where a significant change of an image property occurs. For example, an interest point can be a point in an image where a significant change in color, intensity, or texture occurs. Example interest point can be corners or edges of features of an image. By identifying interest points, an image processing system can map features between multiple images taken from different positions or at different times, in order to estimate parameters describing geometric transforms between images. In the example of  FIG. 1 , example interest points can correspond to any distinctive visible features of the doormat, such as edges of letters of the word “WELCOME.” An interest point  206  can be represented, for example, by a two-dimensional pixel coordinate location of the center of the interest point. 
     The extracted interest points are aggregated over the photometric adapted images and projected back to the reference image  110 . By aggregating over multiple images, the interest point extractor  166  extracts interest points with high stability and repeatability. To aggregate the information from multiple images, the interest point extractor  116  determines a probability of each interest point as well as the frequency of detecting a particular interest point at a particular location. 
     The probability is a measure of the strength of that interest point. The frequency is a measure of repeatability of the interest point. A strong interest point with a high probability is an interest point that has a well-defined position on a region of the object  105 . The well-defined position of the interest point is stable under local and global perturbations in the image such as illumination and brightness variations. 
     Both the criteria of probability and frequency are used in order to generate robust representation for the object  105 . The interest points are sorted and filtered based on their aggregated strength and repeatability. The interest point extractor  116  then selects interest points that meet probability and repeatability criteria, in order to obtain a set of robust interest points on the reference image  110 . In some implementations, the criteria can include a threshold probability score, a threshold repeatability score, or both. Interest points with scores above the threshold scores may be selected, while interest points with scores below the threshold scores may be discarded. 
     The interest point extractor provides interest points to a descriptor aggregator  210  and a center mapper  220 . As the interest points are aggregated from the homographic and photometric augmentations, the descriptor aggregator  210  aggregates descriptors for each interest point in the augmented images. The descriptors can include feature vectors describing the distinguishable appearance of local regions around each interest point. In some examples, local descriptors can be obtained using computer vision algorithms such as SIFT, SURF, and ORB. In some examples, local descriptors can be deep descriptors obtained using deep learning methods such as SuperPoint, UnsuperPoint, and KP2D and so on. 
     The descriptor aggregator  210  aggregates the descriptors per interest point and generates a list of interest point descriptors  212 . Since the interest point descriptors  212  are generated based on homographic and photometric adapted images, the descriptors can include descriptions of scale, gradient change, illumination, brightness, contrast, shade, orientation, distance from camera, etc. 
     Because the selected interest points have high frequency, the size of the descriptor list per interest point may be large. To compress the representation per interest point, the descriptor aggregator can apply a clustering algorithm, e.g. density-based spatial clustering of applications with noise (DBSCAN), to the list of descriptors per interest point. The descriptor aggregator  210  can then replace the list with descriptors for the center of the clusters. This produces a robust and compressed local representation model for the object  105 . Other approaches like principal component analysis (PCA) or dictionary learning can be employed as well to learn a representative subspace for each interest point. In this way, descriptors for an interest point can describe the local region around the feature point. 
     The center mapper  220  identifies the relative center locations  214  for each interest point relative to the center of the object in the reference image  110 . The center mapper  220  may determine a location of the object center in the reference image  110 , e.g., using geometric computations. The geometric computations of the object center can be based on properties (shape, size, etc.) of the object  105 . To obtain the relative center location, the center mapper  220  determines the relative location of the computed object center with respect to each interest point  206 . 
     In some examples, the relative center location can be a two-dimensional pixel offset between the interest point and the center of the object. For example, for an object center positioned at coordinate [x 1 ,y 1 ] and an interest point positioned at coordinate [x 2 ,y 2 ], the relative center location can be represented as an offset [x 2 -x 1 , y 2 -y 1 ]. The relative center location information is linked to each local descriptor and is maintained after the descriptor compression process. 
     The object representation  120  includes the interest points  206  projected back to the reference image  110 . Each interest point is linked to the corresponding interest point descriptors  212  and the relative center location  214 . The object representation  120  is a robust local representation for the object  105 . 
       FIG. 3  illustrates an example system  300  for localizing an object in a sample image using the generated object representation  120 . 
     Given a sample image  130  that includes a depiction of the object  105  in it, the localization engine  115  can localize the object  105 . The localization engine can localize the object  105  by determining a valid homography matrix  320  that maps the object  105  in the reference image  110  to the depiction of the object  105  in the sample image  130 . The homography matrix  320  can be, for example, a 3×3 matrix that transforms a set of planar points in the reference image  110  to another set of planar points in the sample image  130 . By applying the homography matrix  320  to the reference image  110 , the localization engine can obtain the object location  140  within the sample image  130 . 
     The localization engine  115  localizes the object  105  using the interest point matcher  122 , the center estimator  124 , and the homography estimator  126 . The interest point matcher  122  includes an interest point extractor  302  and an interest point comparator  306 . The interest point extractor  302  extracts local interest points and their descriptors from the sample image  130  in the same way that the interest point extractor  116  extracts interest points and their descriptors from the adapted images. The interest point extractor  302  provides sample image interest points  304  to the interest point comparator  306 . 
     The interest point comparator  306  compares the sample image interest points  304  to the object representation  120 . The interest point comparator  306  generates interest point matched pairs  310 . An interest point matched pair includes an interest point from the sample image  130  and a matching interest point from the object representation  120 . Matching interest points can be interest points that have the same or similar descriptors. For example, a similarity between the descriptors may meet similarity criteria. The interest point comparator  306  outputs the interest point matched pairs  310 , including the descriptor information for the matched pairs. 
     The center estimator  124  determines an estimated object center  312  based on the interest points matched pairs  310 . The center estimator  124  uses the relative center information associated with the matched descriptors to estimate the center location of the object  105  in the sample image  130 . Because the object representation  120  includes relative center locations  214  for each interest point, each matched descriptor may include an estimated location of the object center relative to the respective interest point. The center estimator  124  can use the relative center information and the position of the matched interest point from the object representation to estimate the position of the center of the object  105  in the sample image  130 . 
     For example, a particular interest point matched pair includes a particular sample image interest point and a particular matching interest point from the object representation  120 . The particular matching interest point from the object representation  120  includes relative center location information indicating an offset of the center of the doormat relative to the particular matching interest point. Based on the position of the particular sample image interest point in the sample image  130 , and the relative position of the center of the object relative to the particular matching interest point in the object representation  120 , the center estimator  124  can estimate a center location of the doormat in the sample image  130 . 
     The center estimator  124  can repeat estimating the center location of the object  105  in the sample image based on descriptors for multiple interest point matched pairs  310 . The center estimator  124  can then cluster the multiple estimated locations using a clustering algorithm like DB SCAN to find the biggest cluster where the center estimates are located. The centroid of this largest cluster provides an estimated object center  312 . Because the estimated object center  312  is determined from a number of interest point matched pairs  310 , the estimated object center is well localized. 
     The homography estimator  126  generates a homography matrix based on the interest point matched pairs  310  and the estimated object center  312 . The homography estimator  126  can use a random sample consensus (RANSAC)-based approach to obtain a robust and accurate estimate of the homography matrix. 
     The inputs to the homography estimator  126  include the interest point matched pairs  310  and the estimated object center  312 . In some examples, the homography estimator  126  may receive additional information such as an area of interest of the sample image  130  where the object  105  is expected to be. For example, for a camera field of view that includes a porch, the doormat may be expected to be located on the porch. Thus, the porch may be identified as an area of interest. Other additional inputs to the homography estimator  126  can include an estimated size of the object  105 . The estimated size of the object may be based on previous localizations of the object  105 . 
     The homography estimator  126  includes a pair selector  314  and a homography validator  318 . The pair selector  314  selects matched pairs and outputs the selected pairs  316  to the homography validator  318 . The homography validator  318  computes homographies based on the selected pairs  316  and computes a score for each estimated homography using an iterative RANSAC process  315 . The homography validator  318  can rank the homographies based on their homography scores. A process for validating homography estimates, computing homography scores, and generating a valid homography matrix  320  is described with reference to  FIG. 4 . 
       FIG. 4  shows a process  400  of homography estimation performed by the homography estimator  126 . The homography estimator  126  receives interest point matched pairs  310 . The pair selector  314  picks random matched pairs  316 . For example, in each iteration  315 , the homography estimator may randomly select four pairs of matched interest points. As an example, the homography estimator may select matched pairs including interest points P 1R , P 2R , P 3R , and P 4R  from the reference image  110  matched with interest points P 1S , P 3S , P 3S , and P 4S  from the sample image, respectively. The four pairs of selected matched interest points can thus be represented as (P 1R , P 1S ), (P 2R , P 2S ), (P 3R , P 3S ), and (P 4R , P 4S ). 
     The homography validator  318  determines whether selected pairs  406  are cycle consistent  408 . The homography validator  318  can test the selected pairs  406  for cyclic consistency by determining whether the order of the points is the same in the reference image  110  and in the sample image  130 . For example, if an order of points in a clockwise direction around the reference image  110  is P 1R , P 3R , P 4R , P 2R , then a consistent order in the sample image  130  would be P 1S , P 3S , P 4S , P 2S . If the selected pairs  406  are not cycle consistent  408 , the selected pairs are discarded, and another set of pairs is selected. 
     If the selected pairs are cycle consistent, the homography validator  318  performs homography estimation  410 . During homography estimation  410 , a homography matrix is estimated using the selected four pairs of matched points. 
     The estimated homography is validated  412  before being accepted. The homography validator  318  validates the estimated homography by imposing a number of constraints. 
     An example constraint can be to verify that corner points of the shape of the object  105  are all located within the sample image  130 . To validate the projected shape, the corners of object  105  are projected from the reference image  110  to the sample image  130 . For the homography to be valid all projected corner points should be positive, e.g., should be located inside the sample image  130 . 
     Another example constraint can be to verify that the size of the object meets size criteria. For example, the size of the object can be compared to a minimum size. The minimum size may be, for example, 3000 pixels, 2500 pixels, or 2000 pixels. 
     Another example constraint can be to verify that the shape of the object is convex. The shape is convex if, for any two points in the shape, the straight line segment joining them lies entirely within the shape. 
     If any of the constraints are not satisfied, the homography is invalidated, the selected pairs are discarded, and new pairs are selected. If the constraints are satisfied, homography estimation is valid  412 , and the homography validator  318  computes  414  a homography score  416 . 
     For valid homographies, the homography score  416  is computed for the estimated homography for the selected pairs. The score can include multiple elements. 
     An example element of the homography score  416  is normalized center error. To determine normalized center error, the center of the object shape is estimated based on identified corners of the shape. The estimated center of the shape is compared with the estimated object center  312  that was computed by the center estimator  124  using all of the interest point matched pairs  310 . The error between the center of the shape based on the selected pairs and the estimated object center  312  can be measured, for example, as a distance in pixels. The error is then normalized by the largest diagonal of the shape. Thus, the normalized center error is a value less than 1.0. A normalized center error close to 0.0 results in a higher score, while the score decreases as the normalized center error trends away from 0.0. 
     Another example element of the homography score  416  is a size ratio. The homography estimator  126  may receive input indicating a size of the object  105  from previous localizations. For example, the size of the object  105  from previous localizations can include an area of the object as measured in square pixels. In another example, the size of the object  105  from previous localizations can include one or more dimensions of the object as measured in pixels. A size ratio is determined between the current object size based on the estimated homography, and the previous object size. The size ratio is computed by dividing the smaller value by the larger value, so that the resulting ratio is less than 1.0. A size ratio close to 1.0 results in a higher score, while the score decreases as the size ratio trends away from 1.0. 
     Another example element of the homography score  416  is a side ratio. A side ratio can be computed for an object having a polygon shape with parallel sides of similar length. For example, a side ratio can be computed for both pairs of parallel sides of a rectangular object, such as the doormat. A side ratio can also be computed for a parallelogram, a hexagon, an octagon, etc. The side ratio is a ratio between the lengths of parallel sides of the polygon. The side ratio is computed by dividing the smaller value by the larger value, so that the resulting side ratio is less than 1.0. A side ratio close to 1.0 results in a higher score, while the score decreases as the ratio trends away from 1.0. 
     The side ratio can be computed for multiple pairs of sides of the shape. For example, for a polygon with N sides, where N is an even number, the side ratio can be computed for N/2 pairs of parallel sides. As an example, for an object with a regular hexagon shape (N=6), the side ratio can be computed for each of N/2=3 pairs of parallel sides. 
     Another example element of the homography score  416  is an inlier ratio. To determine the inlier ratio, interest points from the reference image  110  are projected to the sample image  130  using the estimated homography matrix. A projection error is computed between the projected interest points and the respective matched interest points in the sample image  130 . Inliers can be defined as the interest points for which the projection error meets criteria. For example, the projection error may meet criteria if the projection error is below a threshold, e.g., two pixels, three pixels, or five pixels. The ratio of the number of inliers to the total number of matched points is the inlier ratio. Thus, the inlier ratio is a value less than 1.0. An inlier ratio close to 1.0 results in a higher score, while the score decreases as the ratio trends away from 1.0. 
     To compute  414  the homography score  416 , a weighted sum of the score elements is computed. The weights are positive values that add to 1.0. Equation 1 is an example equation for calculating the score. In Equation 1, N is the number of sides of the polygon. 
     
       
         
           
             
               
                 
                   Score 
                   = 
                   
                     
                       W 
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       1 
                       × 
                       
                         ( 
                         
                           1 
                           - 
                           
                             normalized_center 
                             ⁢ 
                             _error 
                           
                         
                         ) 
                       
                     
                     + 
                     
                       W 
                       ⁢ 
                       
                           
                       
                       ⁢ 
                       2 
                       × 
                       
                         ( 
                         size_ratio 
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                       W 
                       ⁢ 
                       
                           
                       
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                               2 
                               N 
                             
                             × 
                             
                               ( 
                               
                                 
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                                     N 
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                                 ⁢ 
                                 
                                   side 
                                   ⁢ 
                                   
                                       
                                   
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                                     ⁢ 
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                               ) 
                             
                           
                           + 
                           
                             W 
                             ⁢ 
                             
                                 
                             
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                             4 
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                               ( 
                               inlier_ratio 
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                   Equation 
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                   1 
                 
               
             
           
         
       
     
     Though the described example score includes four elements, the score can include more or fewer elements, in any combination. For example, in some cases the score may be calculated using the elements of normalized center ratio, size ratio, and inlier ratio, but might not include the element of side ratio. In some cases the score may be calculated using elements of normalized center ratio and inlier ratio, but might not include size ratio or side ratio. 
     The process of selecting matched pairs and computing homography scores is iterated  315 , e.g., 1,000 times, 2,000, times, or 3,000 times. After performing the iterative RANSAC process, top homographies  418  are selected based on the homography scores. For example the homography validator  318  may select the top five estimated homographies with the highest scores. These top homographies  418  then go through additional stages of refinement and validation. 
     For each of the top homographies  418 , the homography validator  318  obtains inliers  419 . As described above, inliers are the interest points for which the projection error meets criteria. The homography validator  318  then performs homography estimation  420  using the inliers  419 . The homography matrix is re-estimated using the inlier matched points. 
     The homography validator  318  determines if the homography estimation is valid  422  using the same validation process as in step  412 . If none of the top five estimates result in a valid refined homography, no result is returned. Such a situation may occur if not enough correct matched pairs are found. For example, not enough correct matched pairs may be found when the photometric conditions of the sample image are poor, when the object in the sample image is far from the camera  102 , or when the object  105  is not found in the area of interest. If the homography estimation is not valid, then no solution is found and the homography validator  318  does not output a homography matrix. In some examples, if the homography estimation is not valid, another set of top homographies  418  are selected to undergo the process of validation and refinement as described in steps  419 ,  420 , and  422 . 
     If the homography estimation is valid, the homography estimator  126  outputs the valid homography matrix  320 . The valid homography matrix  424  is provided to the homographic transformer  322 . The homographic transformer  322  uses the valid homography matrix  424  to project the reference image of the object to the sample image in order to obtain the object location  140 . The object location  140  includes the estimated center location and the projection of the object corners to the sample image  130 . 
       FIG. 5  is a flow diagram of an example process  500  for object localization in video. The process  500  can be performed by a computing system, e.g., the camera  102 . In some implementations, the process  500  can be performed by another computing system, e.g., a control unit or a monitoring server of a property monitoring system. In some implementations, a first computer may perform certain actions of the process  500 , and a second computer may perform certain other actions of the process  500 . For example, a first computer may perform steps  502  through  508 , and a second computer may perform steps  510  to  515 . 
     The process  500  includes obtaining a reference image of an object ( 502 ). For example, the camera  102  obtains the reference image  110  of the object  105 . 
     The process  500  includes generating, from the reference image, homographic adapted images ( 504 ). For example, the homographic adaptor  112  generates, from the reference image  110 , the homographic adapted images  202 . The homographic adapted images  202  show the object  105  at various locations, orientations, and distances with respect to the camera. 
     The process  500  can include applying, to the homographic adapted images, photometric adaptation that shows the object at various lighting conditions; and determining the interest points from the photometrically adapted homographic images. For example, the homographic adapted images  202  may be adapted by a photometric adaptor  114 . The photometric adaptor generates photometric adapted images  204 . The photometric adapted images  204  show the object  105  with various illumination levels, brightness, contrast, etc. 
     The process  500  includes determining interest points from the homographic adapted images ( 506 ). For example, the interest point extractor  116  determines interest points  206  from the homographic and photometric adapted images  204 . For example, the interest point extractor  116  can determine an interest point  206  that has a coordinate location of [0150,0275]. 
     The process  500  includes determining locations of a center of the object in the homographic adapted images relative to the interest points ( 508 ). For example, the center mapper  220  determines relative center locations  214  of the object  105  relative to each of the interest points  206 . In some examples, the process  500  includes determining the locations of the center of the object in the homographic adapted images relative to the interest points by determining a two-dimensional pixel offset between each interest point and the center of the object. For example, for an object center positioned at coordinate [0100,0200] and an interest point positioned at coordinate [0150,0275], the relative center location can be represented as an offset [0050,0075]. 
     The process  500  includes obtaining a sample image of the object ( 510 ). For example, the camera  102  captures the sample image  130  of the object  105 . 
     The process  500  includes identifying matched pairs of interest points between the homographic adapted images and the sample image ( 512 ). For example, the interest point matcher  122  identifies interest points in the object representation  120  that match the sample image interest points  304 , where the object representation  120  was generated based on the homographic adapted images  202 . 
     In some implementations, each interest point is associated with one or more descriptors, and the process  500  includes matching the interest point from the homographic images and the interest point in the sample image based on a similarity of the respective associated one or more descriptors. For example, the interest point extractor  116  can extract the interest points  206  and the corresponding descriptors from the photometric adapted images  204 . The descriptors can include feature vectors representing characteristics of the interest points  206 . The interest point comparator  306  can determine a similarity between descriptors of interest point and determine whether the similarity satisfies similarity criteria. 
     In some implementations, the process  500  includes generating, from the homographic adapted images, an object representation for the object. The object representation can include the interest points from the homographic adapted images, and for each interest point, one or more descriptors associated with the interest point. The object representation can also include a location of the center of the object relative to the interest point. The process  500  can include comparing the object representation for the object to the sample image to identify the matched pairs of interest points. For example, the representation engine  108  generates the object representation  120  by projecting the interest points  206  to the reference image  110 . The interest point comparator  306  can compare the object representation  120  to the sample image interest points  304 . The interest point comparator  306  outputs the interest point matched pairs  310 . 
     The process  500  includes determining a location of the object in the sample image based on the locations of the center of the object in the homographic adapted images relative to the matched pairs ( 515 ). For example, the localization engine  115  determines the object location  140  based on the estimated object center  312  relative to the matched interest points of the interest point matched pairs  310 . 
     In some implementations, determining the location of the object in the sample image based on the locations of the center of the object in the homographic adapted images relative to the matched pairs includes generating, from the matched pairs, a homography matrix. For example, the homography estimator  126  generates, from the interest point matched pairs  310 , the valid homography matrix  320 . 
     Determining the location of the object can include projecting the reference image of the object to the sample image using the homography matrix. For example, the homographic transformer projects the reference image  110  of the object  105  to the sample image using the valid homography matrix  320  to determine the object location  140 . 
     In some implementations, generating, from the matched pairs, the homography matrix includes iteratively computing homography scores for subsets of the matched pairs. For example, the homography validator  318  iteratively computes homography scores  416  for the selected pairs  316 . 
     In some implementations, the homography estimator  126  can receive data indicating a first estimated size of the object  105 , e.g., a size of the object determined from previous localizations of the object  105 . The homography estimator  126  can determine a second estimated size of the object  105  based on the subset of the matched pairs, e.g., the selected pairs  316 . Computing the homography score can include computing a ratio between the first estimated size of the object and the second estimated size of the object. For example, the homography estimator  126  can compute a size ratio between the first estimated size of the object  105  determined from the previous localizations and the second estimated size of the object  105  determined from the selected pairs  316 . A size ratio closer to 1.0 results in a higher homography score, while a ratio further from 1.0 results in a lower homography score. 
     In some implementations, the homography estimator  126  can determine a length of a first side of the object  105  based on the selected pairs  316 , and a length of a second side of the object  105  based on the selected pairs  316 , where the second side is parallel to the first side. Computing the homography score can include computing a ratio between the length of the first side and the length of the second side. For example, the homography estimator  126  can compute a side ratio between the determined lengths of the parallel sides of the object  105 . A side ratio closer to 1.0 results in a higher homography score, while a ratio further from 1.0 results in a lower homography score. 
     Generating the homography matrix can include selecting subsets of matched pairs based on the computed homography scores. For example, the homography validator  318  can select the selected pairs  316  having the top homographies  418 . The top homographies  418  can be, for example, the six selected pairs having the highest homographies scores and/or having scores that are closest to a value of 1.0. 
     Generating the homography matrix can include generating the homography matrix from the selected subsets of matched pairs. For example, the homography validator  318  can estimate a homography matrix from the selected pairs having the top homographies  418 . The homography validator  318  performs a validation process to determine whether the estimated homography matrix is valid. The homography validator  318  can then output the valid homography matrix  424 . 
     In some implementations, the process  500  includes determining, based on the locations of the center of the object in the homographic adapted images relative to the interest points and based on the identified matched pairs of interest points, a first estimated center location of the object in the sample image. For example, the center estimator  124  determines, based on the interest point matched pairs  310 , the estimated object center  312 . The estimate object center  312  can be, for example, a coordinate position of [0230,0310]. 
     In some implementations, the process  500  includes iteratively computing homography scores for subsets of the matched pairs by determining, based on the subset of the matched pairs, a second estimated center location of the object in the sample image, and determining an error between the first estimated center location of the object in the sample image and the second estimated center location of the object in the sample image. For example, the homography validator  318  can iteratively compute homography scores  416  for the selected pairs  316 . 
     The homography validator  318  can determine, based on the selected pairs  316 , an estimated center location of the object in the sample image of [0220,0320]. The homography validator  318  can determine a center error between the estimated center location of the object of [0220,0320] and the estimated object center  312  of [0230,0310]. The center error can be, e.g., an offset error of [10, +10] or a distance of 14.1. The center error can be normalized, e.g., by a diagonal of the shape of the object, e.g., a diagonal length of 270 pixels, to obtain a normalized center error of 0.05. A normalized error closer to 0.0 results in a higher homography score. 
     In some implementations, the process  500  includes, based on projecting the reference image of the object to the sample image using the homography matrix, determining the location of the center of the object in the sample image and determining locations of corners of the object in the sample image. For example, based on projecting the reference image of the object  105  to the sample image  130  using the valid homography matrix  320 , the homographic transformer  322  can determine the location of the center of the object  105  and the locations of corners of the object  105  in the sample image  130 . 
     In some implementations, determining the location of the object in the sample image includes determining a coordinate location of at least one of a center, an edge, or a corner of the object in the sample image. For example, the object location  140  can include a coordinate location of [0100,0200] for a center of the object, and a coordinate location of [0050,0080] for a corner of the object. The object location  140  can also include a coordinate location of [0060,0090] for a point at an edge of the object. 
     In some implementations, determining the location of the object in the sample image includes determining at least one of a shape, a size, or an orientation of the object in the sample image. For example, the localization engine  115  can determine that the object  105  has a rectangular shape. The localization engine  115  can also determine that the object  105  has a size of twenty-five by thirty-five pixels, and that the object  105  has an orientation of forty-five degrees relative to vertical. 
     In some implementations, the sample image includes an image captured by a camera. The process  500  can include identifying an area of a field of view of the camera that corresponds to the location of the object; and classifying the identified area of the field of view of the camera as an area of interest. For example, the sample image  130  is captured by the camera  102 . The process  500  can include identifying an area of the field of view  111  of the camera  102  that corresponds to the location of the object  105  as an area of interest. 
     The process  500  can include obtaining additional images captured by the camera; and performing an action in response to detecting motion within the classified area of interest of the additional images. For example, the camera  102  can monitor the area of interest corresponding to the object  105  for activity. For example, when the camera  102  detects an object such as a person or package within the area of interest, the camera  102  can perform an action such as generating a notification. 
       FIG. 6  is a diagram illustrating an example of a home monitoring system  600 . The monitoring system  600  includes a network  605 , a control unit  610 , one or more user devices  640  and  650 , a monitoring server  660 , and a central alarm station server  670 . In some examples, the network  605  facilitates communications between the control unit  610 , the one or more user devices  640  and  650 , the monitoring server  660 , and the central alarm station server  670 . 
     The network  605  is configured to enable exchange of electronic communications between devices connected to the network  605 . For example, the network  605  may be configured to enable exchange of electronic communications between the control unit  610 , the one or more user devices  640  and  650 , the monitoring server  660 , and the central alarm station server  670 . The network  605  may include, for example, one or more of the Internet, Wide Area Networks (WANs), Local Area Networks (LANs), analog or digital wired and wireless telephone networks (e.g., a public switched telephone network (PSTN), Integrated Services Digital Network (ISDN), a cellular network, and Digital Subscriber Line (DSL)), radio, television, cable, satellite, or any other delivery or tunneling mechanism for carrying data. Network  605  may include multiple networks or subnetworks, each of which may include, for example, a wired or wireless data pathway. The network  605  may include a circuit-switched network, a packet-switched data network, or any other network able to carry electronic communications (e.g., data or voice communications). For example, the network  605  may include networks based on the Internet protocol (IP), asynchronous transfer mode (ATM), the PSTN, packet-switched networks based on IP, X.25, or Frame Relay, or other comparable technologies and may support voice using, for example, VoIP, or other comparable protocols used for voice communications. The network  605  may include one or more networks that include wireless data channels and wireless voice channels. The network  605  may be a wireless network, a broadband network, or a combination of networks including a wireless network and a broadband network. 
     The control unit  610  includes a controller  612  and a network module  614 . The controller  612  is configured to control a control unit monitoring system (e.g., a control unit system) that includes the control unit  610 . In some examples, the controller  612  may include a processor or other control circuitry configured to execute instructions of a program that controls operation of a control unit system. In these examples, the controller  612  may be configured to receive input from sensors, flow meters, or other devices included in the control unit system and control operations of devices included in the household (e.g., speakers, lights, doors, etc.). For example, the controller  612  may be configured to control operation of the network module  614  included in the control unit  610 . 
     The network module  614  is a communication device configured to exchange communications over the network  605 . The network module  614  may be a wireless communication module configured to exchange wireless communications over the network  605 . For example, the network module  614  may be a wireless communication device configured to exchange communications over a wireless data channel and a wireless voice channel. In this example, the network module  614  may transmit alarm data over a wireless data channel and establish a two-way voice communication session over a wireless voice channel. The wireless communication device may include one or more of a LTE module, a GSM module, a radio modem, cellular transmission module, or any type of module configured to exchange communications in one of the following formats: LTE, GSM or GPRS, CDMA, EDGE or EGPRS, EV-DO or EVDO, UMTS, or IP. 
     The network module  614  also may be a wired communication module configured to exchange communications over the network  605  using a wired connection. For instance, the network module  614  may be a modem, a network interface card, or another type of network interface device. The network module  614  may be an Ethernet network card configured to enable the control unit  610  to communicate over a local area network and/or the Internet. The network module  614  also may be a voice band modem configured to enable the alarm panel to communicate over the telephone lines of Plain Old Telephone Systems (POTS). 
     The control unit system that includes the control unit  610  includes one or more sensors. For example, the monitoring system may include multiple sensors  620 . The sensors  620  may include a lock sensor, a contact sensor, a motion sensor, or any other type of sensor included in a control unit system. The sensors  620  also may include an environmental sensor, such as a temperature sensor, a water sensor, a rain sensor, a wind sensor, a light sensor, a smoke detector, a carbon monoxide detector, an air quality sensor, etc. The sensors  620  further may include a health monitoring sensor, such as a prescription bottle sensor that monitors taking of prescriptions, a blood pressure sensor, a blood sugar sensor, a bed mat configured to sense presence of liquid (e.g., bodily fluids) on the bed mat, etc. In some examples, the health-monitoring sensor can be a wearable sensor that attaches to a user in the home. The health-monitoring sensor can collect various health data, including pulse, heart rate, respiration rate, sugar or glucose level, bodily temperature, or motion data. 
     The sensors  620  can also include a radio-frequency identification (RFID) sensor that identifies a particular article that includes a pre-assigned RFID tag. 
     The control unit  610  communicates with the home automation controls  622  and a camera  630  to perform monitoring. The home automation controls  622  are connected to one or more devices that enable automation of actions in the home. For instance, the home automation controls  622  may be connected to one or more lighting systems and may be configured to control operation of the one or more lighting systems. In addition, the home automation controls  622  may be connected to one or more electronic locks at the home and may be configured to control operation of the one or more electronic locks (e.g., control Z-Wave locks using wireless communications in the Z-Wave protocol). Further, the home automation controls  622  may be connected to one or more appliances at the home and may be configured to control operation of the one or more appliances. The home automation controls  622  may include multiple modules that are each specific to the type of device being controlled in an automated manner. The home automation controls  622  may control the one or more devices based on commands received from the control unit  610 . For instance, the home automation controls  622  may cause a lighting system to illuminate an area to provide a better image of the area when captured by a camera  630 . 
     The camera  630  may be a video/photographic camera or other type of optical sensing device configured to capture images. For instance, the camera  630  may be configured to capture images of an area within a building or home monitored by the control unit  610 . The camera  630  may be configured to capture single, static images of the area and also video images of the area in which multiple images of the area are captured at a relatively high frequency (e.g., thirty images per second). The camera  630  may be controlled based on commands received from the control unit  610 . 
     The camera  630  may be triggered by several different types of techniques. For instance, a Passive Infra-Red (PIR) motion sensor may be built into the camera  630  and used to trigger the camera  630  to capture one or more images when motion is detected. The camera  630  also may include a microwave motion sensor built into the camera and used to trigger the camera  630  to capture one or more images when motion is detected. The camera  630  may have a “normally open” or “normally closed” digital input that can trigger capture of one or more images when external sensors (e.g., the sensors  620 , PIR, door/window, etc.) detect motion or other events. In some implementations, the camera  630  receives a command to capture an image when external devices detect motion or another potential alarm event. The camera  630  may receive the command from the controller  612  or directly from one of the sensors  620 . 
     In some examples, the camera  630  triggers integrated or external illuminators (e.g., Infra-Red, Z-wave controlled “white” lights, lights controlled by the home automation controls  622 , etc.) to improve image quality when the scene is dark. An integrated or separate light sensor may be used to determine if illumination is desired and may result in increased image quality. 
     The camera  630  may be programmed with any combination of time/day schedules, system “arming state”, or other variables to determine whether images should be captured or not when triggers occur. The camera  630  may enter a low-power mode when not capturing images. In this case, the camera  630  may wake periodically to check for inbound messages from the controller  612 . The camera  630  may be powered by internal, replaceable batteries if located remotely from the control unit  610 . The camera  630  may employ a small solar cell to recharge the battery when light is available. Alternatively, the camera  630  may be powered by the controller&#39;s  612  power supply if the camera  630  is co-located with the controller  612 . 
     In some implementations, the camera  630  communicates directly with the monitoring server  660  over the Internet. In these implementations, image data captured by the camera  630  does not pass through the control unit  610  and the camera  630  receives commands related to operation from the monitoring server  660 . 
     The system  600  also includes thermostat  634  to perform dynamic environmental control at the home. The thermostat  634  is configured to monitor temperature and/or energy consumption of an HVAC system associated with the thermostat  634 , and is further configured to provide control of environmental (e.g., temperature) settings. In some implementations, the thermostat  634  can additionally or alternatively receive data relating to activity at a home and/or environmental data at a home, e.g., at various locations indoors and outdoors at the home. The thermostat  634  can directly measure energy consumption of the HVAC system associated with the thermostat, or can estimate energy consumption of the HVAC system associated with the thermostat  634 , for example, based on detected usage of one or more components of the HVAC system associated with the thermostat  634 . The thermostat  634  can communicate temperature and/or energy monitoring information to or from the control unit  610  and can control the environmental (e.g., temperature) settings based on commands received from the control unit  610 . 
     In some implementations, the thermostat  634  is a dynamically programmable thermostat and can be integrated with the control unit  610 . For example, the dynamically programmable thermostat  634  can include the control unit  610 , e.g., as an internal component to the dynamically programmable thermostat  634 . In addition, the control unit  610  can be a gateway device that communicates with the dynamically programmable thermostat  634 . In some implementations, the thermostat  634  is controlled via one or more home automation controls  622 . 
     A module  637  is connected to one or more components of an HVAC system associated with a home, and is configured to control operation of the one or more components of the HVAC system. In some implementations, the module  637  is also configured to monitor energy consumption of the HVAC system components, for example, by directly measuring the energy consumption of the HVAC system components or by estimating the energy usage of the one or more HVAC system components based on detecting usage of components of the HVAC system. The module  637  can communicate energy monitoring information and the state of the HVAC system components to the thermostat  634  and can control the one or more components of the HVAC system based on commands received from the thermostat  634 . 
     In some examples, the system  600  further includes one or more robotic devices  690 . The robotic devices  690  may be any type of robots that are capable of moving and taking actions that assist in home monitoring. For example, the robotic devices  690  may include drones that are capable of moving throughout a home based on automated control technology and/or user input control provided by a user. In this example, the drones may be able to fly, roll, walk, or otherwise move about the home. The drones may include helicopter type devices (e.g., quad copters), rolling helicopter type devices (e.g., roller copter devices that can fly and roll along the ground, walls, or ceiling) and land vehicle type devices (e.g., automated cars that drive around a home). In some cases, the robotic devices  690  may be devices that are intended for other purposes and merely associated with the system  600  for use in appropriate circumstances. For instance, a robotic vacuum cleaner device may be associated with the monitoring system  600  as one of the robotic devices  690  and may be controlled to take action responsive to monitoring system events. 
     In some examples, the robotic devices  690  automatically navigate within a home. In these examples, the robotic devices  690  include sensors and control processors that guide movement of the robotic devices  690  within the home. For instance, the robotic devices  690  may navigate within the home using one or more cameras, one or more proximity sensors, one or more gyroscopes, one or more accelerometers, one or more magnetometers, a global positioning system (GPS) unit, an altimeter, one or more sonar or laser sensors, and/or any other types of sensors that aid in navigation about a space. The robotic devices  690  may include control processors that process output from the various sensors and control the robotic devices  690  to move along a path that reaches the desired destination and avoids obstacles. In this regard, the control processors detect walls or other obstacles in the home and guide movement of the robotic devices  690  in a manner that avoids the walls and other obstacles. 
     In addition, the robotic devices  690  may store data that describes attributes of the home. For instance, the robotic devices  690  may store a floorplan and/or a three-dimensional model of the home that enables the robotic devices  690  to navigate the home. During initial configuration, the robotic devices  690  may receive the data describing attributes of the home, determine a frame of reference to the data (e.g., a home or reference location in the home), and navigate the home based on the frame of reference and the data describing attributes of the home. Further, initial configuration of the robotic devices  690  also may include learning of one or more navigation patterns in which a user provides input to control the robotic devices  690  to perform a specific navigation action (e.g., fly to an upstairs bedroom and spin around while capturing video and then return to a home charging base). In this regard, the robotic devices  690  may learn and store the navigation patterns such that the robotic devices  690  may automatically repeat the specific navigation actions upon a later request. 
     In some examples, the robotic devices  690  may include data capture and recording devices. In these examples, the robotic devices  690  may include one or more cameras, one or more motion sensors, one or more microphones, one or more biometric data collection tools, one or more temperature sensors, one or more humidity sensors, one or more air flow sensors, and/or any other types of sensors that may be useful in capturing monitoring data related to the home and users in the home. The one or more biometric data collection tools may be configured to collect biometric samples of a person in the home with or without contact of the person. For instance, the biometric data collection tools may include a fingerprint scanner, a hair sample collection tool, a skin cell collection tool, and/or any other tool that allows the robotic devices  690  to take and store a biometric sample that can be used to identify the person (e.g., a biometric sample with DNA that can be used for DNA testing). 
     In some implementations, the robotic devices  690  may include output devices. In these implementations, the robotic devices  690  may include one or more displays, one or more speakers, and/or any type of output devices that allow the robotic devices  690  to communicate information to a nearby user. 
     The robotic devices  690  also may include a communication module that enables the robotic devices  690  to communicate with the control unit  610 , each other, and/or other devices. The communication module may be a wireless communication module that allows the robotic devices  690  to communicate wirelessly. For instance, the communication module may be a Wi-Fi module that enables the robotic devices  690  to communicate over a local wireless network at the home. The communication module further may be a 900 MHz wireless communication module that enables the robotic devices  690  to communicate directly with the control unit  610 . Other types of short-range wireless communication protocols, such as Bluetooth, Bluetooth LE, Z-wave, Zigbee, etc., may be used to allow the robotic devices  690  to communicate with other devices in the home. In some implementations, the robotic devices  690  may communicate with each other or with other devices of the system  600  through the network  605 . 
     The robotic devices  690  further may include processor and storage capabilities. The robotic devices  690  may include any suitable processing devices that enable the robotic devices  690  to operate applications and perform the actions described throughout this disclosure. In addition, the robotic devices  690  may include solid-state electronic storage that enables the robotic devices  690  to store applications, configuration data, collected sensor data, and/or any other type of information available to the robotic devices  690 . 
     The robotic devices  690  are associated with one or more charging stations. The charging stations may be located at predefined home base or reference locations in the home. The robotic devices  690  may be configured to navigate to the charging stations after completion of tasks needed to be performed for the monitoring system  600 . For instance, after completion of a monitoring operation or upon instruction by the control unit  610 , the robotic devices  690  may be configured to automatically fly to and land on one of the charging stations. In this regard, the robotic devices  690  may automatically maintain a fully charged battery in a state in which the robotic devices  690  are ready for use by the monitoring system  600 . 
     The charging stations may be contact based charging stations and/or wireless charging stations. For contact based charging stations, the robotic devices  690  may have readily accessible points of contact that the robotic devices  690  are capable of positioning and mating with a corresponding contact on the charging station. For instance, a helicopter type robotic device may have an electronic contact on a portion of its landing gear that rests on and mates with an electronic pad of a charging station when the helicopter type robotic device lands on the charging station. The electronic contact on the robotic device may include a cover that opens to expose the electronic contact when the robotic device is charging and closes to cover and insulate the electronic contact when the robotic device is in operation. 
     For wireless charging stations, the robotic devices  690  may charge through a wireless exchange of power. In these cases, the robotic devices  690  need only locate themselves closely enough to the wireless charging stations for the wireless exchange of power to occur. In this regard, the positioning needed to land at a predefined home base or reference location in the home may be less precise than with a contact based charging station. Based on the robotic devices  690  landing at a wireless charging station, the wireless charging station outputs a wireless signal that the robotic devices  690  receive and convert to a power signal that charges a battery maintained on the robotic devices  690 . 
     In some implementations, each of the robotic devices  690  has a corresponding and assigned charging station such that the number of robotic devices  690  equals the number of charging stations. In these implementations, the robotic devices  690  always navigate to the specific charging station assigned to that robotic device. For instance, a first robotic device may always use a first charging station and a second robotic device may always use a second charging station. 
     In some examples, the robotic devices  690  may share charging stations. For instance, the robotic devices  690  may use one or more community charging stations that are capable of charging multiple robotic devices  690 . The community charging station may be configured to charge multiple robotic devices  690  in parallel. The community charging station may be configured to charge multiple robotic devices  690  in serial such that the multiple robotic devices  690  take turns charging and, when fully charged, return to a predefined home base or reference location in the home that is not associated with a charger. The number of community charging stations may be less than the number of robotic devices  690 . 
     In addition, the charging stations may not be assigned to specific robotic devices  690  and may be capable of charging any of the robotic devices  690 . In this regard, the robotic devices  690  may use any suitable, unoccupied charging station when not in use. For instance, when one of the robotic devices  690  has completed an operation or is in need of battery charge, the control unit  610  references a stored table of the occupancy status of each charging station and instructs the robotic device to navigate to the nearest charging station that is unoccupied. 
     The system  600  further includes one or more integrated security devices  680 . The one or more integrated security devices may include any type of device used to provide alerts based on received sensor data. For instance, the one or more control units  610  may provide one or more alerts to the one or more integrated security input/output devices  680 . Additionally, the one or more control units  610  may receive one or more sensor data from the sensors  620  and determine whether to provide an alert to the one or more integrated security input/output devices  680 . 
     The sensors  620 , the home automation controls  622 , the camera  630 , the thermostat  634 , and the integrated security devices  680  may communicate with the controller  612  over communication links  624 ,  626 ,  628 ,  632 ,  638 , and  684 . The communication links  624 ,  626 ,  628 ,  632 ,  638 , and  684  may be a wired or wireless data pathway configured to transmit signals from the sensors  620 , the home automation controls  622 , the camera  630 , the thermostat  634 , and the integrated security devices  680  to the controller  612 . The sensors  620 , the home automation controls  622 , the camera  630 , the thermostat  634 , and the integrated security devices  680  may continuously transmit sensed values to the controller  612 , periodically transmit sensed values to the controller  612 , or transmit sensed values to the controller  612  in response to a change in a sensed value. 
     The communication links  624 ,  626 ,  628 ,  632 ,  638 , and  684  may include a local network. The sensors  620 , the home automation controls  622 , the camera  630 , the thermostat  634 , and the integrated security devices  680 , and the controller  612  may exchange data and commands over the local network. The local network may include 802.11 “Wi-Fi” wireless Ethernet (e.g., using low-power Wi-Fi chipsets), Z-Wave, Zigbee, Bluetooth, “Homeplug” or other “Powerline” networks that operate over AC wiring, and a Category 5 (CATS) or Category 6 (CAT6) wired Ethernet network. The local network may be a mesh network constructed based on the devices connected to the mesh network. 
     The monitoring server  660  is an electronic device configured to provide monitoring services by exchanging electronic communications with the control unit  610 , the one or more user devices  640  and  650 , and the central alarm station server  670  over the network  605 . For example, the monitoring server  660  may be configured to monitor events generated by the control unit  610 . In this example, the monitoring server  660  may exchange electronic communications with the network module  614  included in the control unit  610  to receive information regarding events detected by the control unit  610 . The monitoring server  660  also may receive information regarding events from the one or more user devices  640  and  650 . 
     In some examples, the monitoring server  660  may route alert data received from the network module  614  or the one or more user devices  640  and  650  to the central alarm station server  670 . For example, the monitoring server  660  may transmit the alert data to the central alarm station server  670  over the network  605 . 
     The monitoring server  660  may store sensor and image data received from the monitoring system and perform analysis of sensor and image data received from the monitoring system. Based on the analysis, the monitoring server  660  may communicate with and control aspects of the control unit  610  or the one or more user devices  640  and  650 . 
     The monitoring server  660  may provide various monitoring services to the system  600 . For example, the monitoring server  660  may analyze the sensor, image, and other data to determine an activity pattern of a resident of the home monitored by the system  600 . In some implementations, the monitoring server  660  may analyze the data for alarm conditions or may determine and perform actions at the home by issuing commands to one or more of the controls  622 , possibly through the control unit  610 . 
     The monitoring server  660  can be configured to provide information (e.g., activity patterns) related to one or more residents of the home monitored by the system  600 . For example, one or more of the sensors  620 , the home automation controls  622 , the camera  630 , the thermostat  634 , and the integrated security devices  680  can collect data related to a resident including location information (e.g., if the resident is home or is not home) and provide location information to the thermostat  634 . 
     The central alarm station server  670  is an electronic device configured to provide alarm monitoring service by exchanging communications with the control unit  610 , the one or more user devices  640  and  650 , and the monitoring server  660  over the network  605 . For example, the central alarm station server  670  may be configured to monitor alerting events generated by the control unit  610 . In this example, the central alarm station server  670  may exchange communications with the network module  614  included in the control unit  610  to receive information regarding alerting events detected by the control unit  610 . The central alarm station server  670  also may receive information regarding alerting events from the one or more user devices  640  and  650  and/or the monitoring server  660 . 
     The central alarm station server  670  is connected to multiple terminals  672  and  674 . The terminals  672  and  674  may be used by operators to process alerting events. For example, the central alarm station server  670  may route alerting data to the terminals  672  and  674  to enable an operator to process the alerting data. The terminals  672  and  674  may include general-purpose computers (e.g., desktop personal computers, workstations, or laptop computers) that are configured to receive alerting data from a server in the central alarm station server  670  and render a display of information based on the alerting data. For instance, the controller  612  may control the network module  614  to transmit, to the central alarm station server  670 , alerting data indicating that a sensor  620  detected motion from a motion sensor via the sensors  620 . The central alarm station server  670  may receive the alerting data and route the alerting data to the terminal  672  for processing by an operator associated with the terminal  672 . The terminal  672  may render a display to the operator that includes information associated with the alerting event (e.g., the lock sensor data, the motion sensor data, the contact sensor data, etc.) and the operator may handle the alerting event based on the displayed information. 
     In some implementations, the terminals  672  and  674  may be mobile devices or devices designed for a specific function. Although  FIG. 6  illustrates two terminals for brevity, actual implementations may include more (and, perhaps, many more) terminals. 
     The one or more authorized user devices  640  and  650  are devices that host and display user interfaces. For instance, the user device  640  is a mobile device that hosts or runs one or more native applications (e.g., the home monitoring application  642 ). The user device  640  may be a cellular phone or a non-cellular locally networked device with a display. The user device  640  may include a cell phone, a smart phone, a tablet PC, a personal digital assistant (“PDA”), or any other portable device configured to communicate over a network and display information. For example, implementations may also include Blackberry-type devices (e.g., as provided by Research in Motion), electronic organizers, iPhone-type devices (e.g., as provided by Apple), iPod devices (e.g., as provided by Apple) or other portable music players, other communication devices, and handheld or portable electronic devices for gaming, communications, and/or data organization. The user device  640  may perform functions unrelated to the monitoring system, such as placing personal telephone calls, playing music, playing video, displaying pictures, browsing the Internet, maintaining an electronic calendar, etc. 
     The user device  640  includes a home monitoring application  652 . The home monitoring application  642  refers to a software/firmware program running on the corresponding mobile device that enables the user interface and features described throughout. The user device  640  may load or install the home monitoring application  642  based on data received over a network or data received from local media. The home monitoring application  642  runs on mobile devices platforms, such as iPhone, iPod touch, Blackberry, Google Android, Windows Mobile, etc. The home monitoring application  642  enables the user device  640  to receive and process image and sensor data from the monitoring system. 
     The user device  640  may be a general-purpose computer (e.g., a desktop personal computer, a workstation, or a laptop computer) that is configured to communicate with the monitoring server  660  and/or the control unit  610  over the network  605 . The user device  640  may be configured to display a smart home user interface  652  that is generated by the user device  640  or generated by the monitoring server  660 . For example, the user device  640  may be configured to display a user interface (e.g., a web page) provided by the monitoring server  660  that enables a user to perceive images captured by the camera  630  and/or reports related to the monitoring system. Although  FIG. 6  illustrates two user devices for brevity, actual implementations may include more (and, perhaps, many more) or fewer user devices. 
     In some implementations, the one or more user devices  640  and  650  communicate with and receive monitoring system data from the control unit  610  using the communication link  638 . For instance, the one or more user devices  640  and  650  may communicate with the control unit  610  using various local wireless protocols such as Wi-Fi, Bluetooth, Z-wave, Zigbee, HomePlug (ethernet over power line), or wired protocols such as Ethernet and USB, to connect the one or more user devices  640  and  650  to local security and automation equipment. The one or more user devices  640  and  650  may connect locally to the monitoring system and its sensors and other devices. The local connection may improve the speed of status and control communications because communicating through the network  605  with a remote server (e.g., the monitoring server  660 ) may be significantly slower. 
     Although the one or more user devices  640  and  650  are shown as communicating with the control unit  610 , the one or more user devices  640  and  650  may communicate directly with the sensors and other devices controlled by the control unit  610 . In some implementations, the one or more user devices  640  and  650  replace the control unit  610  and perform the functions of the control unit  610  for local monitoring and long range/offsite communication. 
     In other implementations, the one or more user devices  640  and  650  receive monitoring system data captured by the control unit  610  through the network  605 . The one or more user devices  640 ,  650  may receive the data from the control unit  610  through the network  605  or the monitoring server  660  may relay data received from the control unit  610  to the one or more user devices  640  and  650  through the network  605 . In this regard, the monitoring server  660  may facilitate communication between the one or more user devices  640  and  650  and the monitoring system. 
     In some implementations, the one or more user devices  640  and  650  may be configured to switch whether the one or more user devices  640  and  650  communicate with the control unit  610  directly (e.g., through link  638 ) or through the monitoring server  660  (e.g., through network  605 ) based on a location of the one or more user devices  640  and  650 . For instance, when the one or more user devices  640  and  650  are located close to the control unit  610  and in range to communicate directly with the control unit  610 , the one or more user devices  640  and  650  use direct communication. When the one or more user devices  640  and  650  are located far from the control unit  610  and not in range to communicate directly with the control unit  610 , the one or more user devices  640  and  650  use communication through the monitoring server  660 . 
     Although the one or more user devices  640  and  650  are shown as being connected to the network  605 , in some implementations, the one or more user devices  640  and  650  are not connected to the network  605 . In these implementations, the one or more user devices  640  and  650  communicate directly with one or more of the monitoring system components and no network (e.g., Internet) connection or reliance on remote servers is needed. 
     In some implementations, the one or more user devices  640  and  650  are used in conjunction with only local sensors and/or local devices in a house. In these implementations, the system  600  includes the one or more user devices  640  and  650 , the sensors  620 , the home automation controls  622 , the camera  630 , and the robotic devices  690 . The one or more user devices  640  and  650  receive data directly from the sensors  620 , the home automation controls  622 , the camera  630 , and the robotic devices  690 , and sends data directly to the sensors  620 , the home automation controls  622 , the camera  630 , and the robotic devices  690 . The one or more user devices  640 ,  650  provide the appropriate interfaces/processing to provide visual surveillance and reporting. 
     In other implementations, the system  600  further includes network  605  and the sensors  620 , the home automation controls  622 , the camera  630 , the thermostat  634 , and the robotic devices  690 , and are configured to communicate sensor and image data to the one or more user devices  640  and  650  over network  605  (e.g., the Internet, cellular network, etc.). In yet another implementation, the sensors  620 , the home automation controls  622 , the camera  630 , the thermostat  634 , and the robotic devices  690  (or a component, such as a bridge/router) are intelligent enough to change the communication pathway from a direct local pathway when the one or more user devices  640  and  650  are in close physical proximity to the sensors  620 , the home automation controls  622 , the camera  630 , the thermostat  634 , and the robotic devices  690  to a pathway over network  605  when the one or more user devices  640  and  650  are farther from the sensors  620 , the home automation controls  622 , the camera  630 , the thermostat  634 , and the robotic devices  690 . 
     In some examples, the system leverages GPS information from the one or more user devices  640  and  650  to determine whether the one or more user devices  640  and  650  are close enough to the sensors  620 , the home automation controls  622 , the camera  630 , the thermostat  634 , and the robotic devices  690  to use the direct local pathway or whether the one or more user devices  640  and  650  are far enough from the sensors  620 , the home automation controls  622 , the camera  630 , the thermostat  634 , and the robotic devices  690  that the pathway over network  605  is required. 
     In other examples, the system leverages status communications (e.g., pinging) between the one or more user devices  640  and  650  and the sensors  620 , the home automation controls  622 , the camera  630 , the thermostat  634 , and the robotic devices  690  to determine whether communication using the direct local pathway is possible. If communication using the direct local pathway is possible, the one or more user devices  640  and  650  communicate with the sensors  620 , the home automation controls  622 , the camera  630 , the thermostat  634 , and the robotic devices  690  using the direct local pathway. If communication using the direct local pathway is not possible, the one or more user devices  640  and  650  communicate with the sensors  620 , the home automation controls  622 , the camera  630 , the thermostat  634 , and the robotic devices  690  using the pathway over network  605 . 
     In some implementations, the system  600  provides end users with access to images captured by the camera  630  to aid in decision making. The system  600  may transmit the images captured by the camera  630  over a wireless WAN network to the user devices  640  and  650 . Because transmission over a wireless WAN network may be relatively expensive, the system  600  can use several techniques to reduce costs while providing access to significant levels of useful visual information (e.g., compressing data, down-sampling data, sending data only over inexpensive LAN connections, or other techniques). 
     In some implementations, a state of the monitoring system and other events sensed by the monitoring system may be used to enable/disable video/image recording devices (e.g., the camera  630 ). In these implementations, the camera  630  may be set to capture images on a periodic basis when the alarm system is armed in an “away” state, but set not to capture images when the alarm system is armed in a “home” state or disarmed. In addition, the camera  630  may be triggered to begin capturing images when the alarm system detects an event, such as an alarm event, a door-opening event for a door that leads to an area within a field of view of the camera  630 , or motion in the area within the field of view of the camera  630 . In other implementations, the camera  630  may capture images continuously, but the captured images may be stored or transmitted over a network when needed. 
     The described systems, methods, and techniques may be implemented in digital electronic circuitry, computer hardware, firmware, software, or in combinations of these elements. Apparatus implementing these techniques may include appropriate input and output devices, a computer processor, and a computer program product tangibly embodied in a machine-readable storage device for execution by a programmable processor. A process implementing these techniques may be performed by a programmable processor executing a program of instructions to perform desired functions by operating on input data and generating appropriate output. The techniques may be implemented in one or more computer programs that are executable on a programmable system including at least one programmable processor coupled to receive data and instructions from, and to transmit data and instructions to, a data storage system, at least one input device, and at least one output device. 
     Each computer program may be implemented in a high-level procedural or object-oriented programming language, or in assembly or machine language if desired; and in any case, the language may be a compiled or interpreted language. Suitable processors include, by way of example, both general and special purpose microprocessors. Generally, a processor will receive instructions and data from a read-only memory and/or a random access memory. Storage devices suitable for tangibly embodying computer program instructions and data include all forms of non-volatile memory, including by way of example semiconductor memory devices, such as Erasable Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM), and flash memory devices; magnetic disks such as internal hard disks and removable disks; magneto-optical disks; and Compact Disc Read-Only Memory (CD-ROM). Any of the foregoing may be supplemented by, or incorporated in, specially designed ASICs (application-specific integrated circuits). 
     It will be understood that various modifications may be made. For example, other useful implementations could be achieved if steps of the disclosed techniques were performed in a different order and/or if components in the disclosed systems were combined in a different manner and/or replaced or supplemented by other components. Accordingly, other implementations are within the scope of the disclosure.