Reducing false detections for night vision cameras

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for reducing camera false detections. One of the methods includes providing, to a neural network of an image classifier that is trained to detect objects of two or more classification types, a feature vector for a respective training image; receiving, from the neural network, an output vector that indicates, for each of the two or more classification types, a likelihood that the respective training image depicts an object of the corresponding classification type; accessing, from two or more ground truth vectors each for one of the two or more classification types, a ground truth vector for the classification type of an object depicted in the training image; and adjusting one or more weights in the neural network using the output vector and the ground truth vector; and storing, in a memory, the image classifier.

BACKGROUND

Properties can be equipped with monitoring systems, e.g., security systems, that include sensors and connected system components. Some residential-based monitoring systems include cameras. Cameras can use computer vision technology to analyze images to detect objects depicted in the images.

SUMMARY

Cameras can employ image analytics for monitoring a residential or a business property. These cameras can operate around the clock, e.g., both day and night to detect any activity caused by an object of interest, e.g., human, animal, or vehicle in and around the property, and alert the owner.

Additionally or alternatively, depending on the class of objects depicted in the images, a system can determine whether to perform an automated action such as turning on lights at the property. For example, the system can turn on lights upon detecting a moving person depicted in an image captured by the camera. In contrast, when the system detects a tree or a spider web depicted in an image, which may be moving due to wind, the system can determine to not provide an alert, not to perform any automated action like turning on lights, or both.

While detecting events at daytime can be achieved with reasonable accuracy using a combination of low-cost solutions like background modeling with optimized deep learning-based classifiers, detection at night can fail frequently with a large drop in accuracy. Detection at night is particularly challenging due to conditions such as low illumination, low contrast, sudden illumination changes (e.g., sudden change in lighting as the vehicle enters the driveway), rain streaks, or spider or spider web movements on the cameras, both of which can have object like movements, shapes, or both. In these challenging conditions at night, a system can have a higher likelihood of producing false classifications even when a state-of-the-art deep learning-based classification technique is deployed.

In general, one innovative aspect of the subject matter described in this specification relates to reducing camera false detections, and can be embodied in methods that include the actions of, for each of a plurality of training images each of which are associated with a classification type from two or more classification types, the plurality of training images including at least one image for each of the two or more classification types: providing, to a neural network of an image classifier that is trained to detect objects of the two or more classification types, a feature vector for the respective training image; receiving, from the neural network, an output vector that indicates, for each of the two or more classification types, a likelihood that the respective training image depicts an object of the corresponding classification type; accessing, from two or more ground truth vectors each for one of the two or more classification types, a ground truth vector for the classification type of an object depicted in the respective training image; and adjusting one or more weights in the neural network using a combination of the output vector and the ground truth vector for the classification type of the objected depicted in the respective training image; and storing, in a memory, the image classifier that includes the neural network for use by a camera to classify objects detected in one or more images captured by the camera.

In general, one innovative aspect of the subject matter described in this specification relates to reducing camera false detections, and can be embodied in methods that include the actions of providing, to a neural network being trained to detect objects of interest, a training image that depicts an object; receiving, from the neural network, an indication that the object is a first class of object of non-interest; determining that the object is a second class of object of non-interest and was incorrectly classified by the neural network as the first class of object of non-interest; and weighting the neural network towards correctly classifying the object as the second class of object of non-interest.

The foregoing and other embodiments can each optionally include one or more of the following features, alone or in combination. In some implementations, a ground truth vector for an image can represent a ground truth label for the image.

In some implementations, each ground truth vector can include a value for each of the two or more classification types. Each ground truth vector can include a highest value for the classification type from the two or more classification types of the object depicted in a corresponding image. Adjusting the one or more weights in the neural network can include combining, for each of the values in the ground truth vector, the respective value from the ground truth vector with a corresponding value from the output vector to generate combined values; generating a training value using the combined values; and adjusting the one or more weights in the neural network using the training value.

In some implementations, each of the two or more classification types can be a classification for either an object of interest or not an object of interest. A particular ground truth vector from the two or more ground truth vectors can include, for each of the two or more classification types, two or more non-negative values each of which are less than one and the sum of which equals one. Multiple, e.g., each, of the two or more non-negative values in the particular ground truth vector can be the same value.

In some implementations, storing, in a memory, the image classifier can include combining the neural network with a binary classifier layer to generate a binary neural network trained to receive a feature vector for an image as input and output a value that indicates whether an object depicted in the image is an object of interest or is not an object of interest; and storing, in a memory, the image classifier that includes the binary neural network for use by an camera to classify objects detected in one or more images captured by the camera.

In some implementations, the method can include providing the image classifier to a camera for use by the camera classifying objects detected in one or more images captured by the camera. Providing the image classifier to the camera can include providing an infrared image classifier that includes the neural network to an infrared camera.

In some implementations, accessing the ground truth vector can include accessing, from three or more ground truth vectors each for one of three or more classification types, the ground truth vector for the classification type of an object depicted in the respective training image. The three or more classification types can include five classification types including a background classification, a spider web classification, a human classification, an animal classification, and a vehicle classification. Accessing the ground truth vector can include accessing, from five ground truth vectors each for one of the five classification types, the ground truth vector for the classification type of an object depicted in the respective training image.

In some implementations, the method can include storing the weighted neural network in memory for use by a camera in classifying one or more images captured by the camera. The neural network can be being trained to detect objects of three or more classes including the first class of object of non-interest, the second class of object of non-interest, and a third class of object of interest. The neural network can be an infrared image classifier.

In some implementations, the method can include selecting a first weight with a first sign based on the first class and the second class both being objects of non-interest; determining, for a second object depicted in a second training image that the neural network incorrectly classified as a third class, i) that the second object is a fourth class and i) one of the third class and the fourth class is a class of object of non-interest and the other of the third class and the fourth class is a class of object of interest; selecting a second weight with a second, different sign based on one of the third class and the fourth class is a class of object of non-interest and the other of the third class and the fourth class is a class of object of interest; and weighting the neural network towards correctly classifying the second object as the fourth class using the second weight. Weighting the neural network towards correctly classifying the object as the second class of object of non-interest can use the first weight.

In some implementations, weighting the neural network can include determining a ground truth vector for the second class of object of non-interest; generating a training value by combining the ground truth vector with an output vector received from the neural network that includes the indication that the object is the first class of object of non-interest; and updating one or more weights in the neural network using the training value.

In some implementations, the method can include creating an image classifier by adding a binary classifier layer to an output layer of the neural network; and transmitting the image classifier to a camera for use classifying an image as depicting an object of interest or an object of non-interest.

The subject matter described in this specification can be implemented in various embodiments and may result in one or more of the following advantages. In some implementations, the systems and methods described in this specification can reduce false classifications. The systems and methods can reduce false classifications using ground truth vectors and vectors output by a neural network, e.g., by combining the two vectors. In some implementations, the systems and methods described in this specification can reduce false classifications by learning for noisy classes separately and/or balancing recall and precision of classification between objects of interest and non-objects of interest using different class weights during training for the different classifications.

DETAILED DESCRIPTION

FIG.1is a block diagram of an example environment100that detects objects. The environment100includes a training system110, a camera150, and a monitoring server160. The training system110provides a trained image classifier to the camera150, and the camera150uses the trained image classifier to detect objects of interest and provide indications to the monitoring server160that indicate that objects of interest were detected. In some implementations, the training system110may be included in the monitoring server160.

The training system110includes a training engine112that trains an image classifier120to generate a trained image classifier. The image classifier120can classify whether or not an image includes an object of interest. The image classifier120includes a neural network122and a binary classifier layer124.

The camera150can receive the trained image classifier from the training system110and then detect objects of interest with the trained image classifier. The camera150may provide indications to the monitoring server160when objects of interest are detected, and otherwise not provide indications that objects of interest were not detected.

For instance, the camera150can use the trained image classifier to analyze an image captured by the camera150. The trained image classifier can generate output that indicates whether the captured image likely depicts an object of interest. When the camera150receives, from the trained image classifier, output that indicates that a captured image likely depicts an object of interest, the camera150can send a message to the monitoring server160that includes data about the object of interest. When the camera150receives, from the trained image classifier, output that indicates that a captured image likely does not depict an object of interest, the camera150can determine to skip sending a message to the monitoring server160.

FIG.2is a block diagram of an image classifier120. The image classifier120can be any appropriate type of image classifier, such as an infrared image classifier, a visible spectrum image classifier, or a combination of both. The image classifier120can be the image classifier114fromFIG.1.

The image classifier120includes the neural network122, e.g., the neural network116, and the binary classifier layer124, e.g., the binary classifier layer118. The neural network122receives, as input, a representation of an image and provides outputs for different classes of classification. Some examples of input include background content126, e.g., a tree or a bush, spider webs128, animals130, vehicles132, and humans134.

In response to receiving the input, the neural network122can classify an image as including background136, a spider web138, a vehicle142, a human144, or an animal140, respectively given the input data. In some examples, the neural network122can include one or more additional classifications, such as plant, water, or both.

A background classification can be used to classify objects other than the other objects for which the neural network122is trained. For example, when the neural network122is trained to detect vehicles, animals, and humans, the neural network122can use the background classification for any other objects. When the neural network122is trained to detect vehicles, animals, humans, and spider webs, the neural network122can use, e.g., be trained to use, the background classification for any other objects.

Sometimes an image may depict multiple objects that belong to different classifications for which the neural network122is trained. In these examples, the neural network122can classify the image as depicting objects in multiple different classifications or a single, primary classification. The primary classification can be for an object that is depicted most prominently in the image; a classification for an object of interest, e.g., animal, human, or vehicle, and not a classification for a non-object of interest, e.g., background or a spider web, to the extent that the neural network122determines that the image likely depicts an object of interest; or another appropriate single classification.

While a neural network for classifying an image as being an object of interest versus a non-object of interest may be trained, such a neural network may not work well due to the challenge of trying to capture the combined distribution of multiple object of interest classes in one single class. For instance, such a neural network can have a low accuracy, for objects of interest, non-objects of interest, or both.

Accordingly, the neural network122classifies the image between multiple different background classes because a generic background classifier may not accurately capture high variation, e.g., between objects of different types, in the background class. Creating a separate class for spider webs, in addition to the other classes for objects of interest, may distribute data between background classes and result in each background class having less noisy data, e.g., a higher accuracy. For example, a classifier trained to only detect spider webs may be more accurate at detecting spider webs than a classifier that is trained to detect both spider webs and background, e.g., and output a single value that indicates whether an image likely depicts either a spider web or background.

Additionally or alternatively, the neural network122can be used only to classify images captured while a camera is in a night mode and a separate different neural network used to classify while the camera is not in the night mode, e.g., a day mode. The night mode may be a mode in which the camera generates images that represent detected infrared light and the day mode may be a mode in which the camera generates images that represent detected light other than infrared light, e.g., visible spectrum light.

Separate classifiers may be used for day mode and night mode as a generic classifier alone may not perform well at classifying events in all varying conditions. This may be due to insufficient number of hard samples mimicking the tough conditions in night mode present in training data. Even with training data covering a wide variety of conditions, a generic classifier might not learn to classify well in both day and night due to large imbalance and variance in the training data. Accordingly, the image classifier120may be used when a camera is in night mode and a different image classifier, e.g., a visible spectrum image classifier, may be used when a camera is not in night mode, e.g., when the camera is in day mode.

The binary classifier layer124receives the output of the neural network122and outputs a binary indication of whether an object of interest is shown in the image. For example, the binary classifier layer124may receive a vector of [0, 1, 0, 0, 0], where going from left to right in the vector, the first element is a binary indication of whether the image is of background136, the second element is a binary indication of whether the image is of a human144, the third element is binary indication of whether the image is of an animal140, the fourth element is a binary indication of whether the image is of a vehicle142, and the fifth element is binary indication of whether the image is of a spider web138. In some implementations, the values in the vector can represent different orders for the classifications136-144.

In the example, the binary classifier layer124may then output an indication whether the image does not depict an object of interest146or depicts an object of interest148. The indication can be a binary output, e.g., with one indicating that the image depicts an object of interest148and zero indicating that the image does not depict an object of interest146, such as a non-object of interest. The binary output can be a final classification type.

In another example, the binary classifier layer124may receive a vector of [1, 0, 0, 0, 0], and output a binary indication that the image is not of an object of interest. This can occur when the input image depicts a tree or a bush or some other type of background content126.

The binary classifier layer124may output that the image is of an object of interest148if the neural network122outputs data that indicates that a human144, an animal140, or a vehicle142was detected in an input image. The binary classifier layer124may output that the image does not depict an object of interest146, or is not of an object of interest, if the neural network122outputs data that indicates that the image is of background content136or a spider web138(or a spider).

In training the neural network122, misclassifications among classes of objects of interest may be permissible, and misclassifications among classes of non-objects of interest may be permissible, but misclassifications of objects of interest as non-objects of interest or vice versa may be penalized, e.g., by the training system110. For example, the camera150may provide the monitoring server160an image when an object of interest is detected irrespective of the class of object of interest that was detected. The camera150might not provide the monitoring server160an image when the image does not depict an object of interest, e.g., when only non-objects of interest are detected, irrespective of the classes of the non-objects of interest that might be detected in the image. Therefore, the neural network122is trained to be a multi-class classifier and the binary classifier layer124can enable the image classifier120to behave like a binary classifier even though the image classifier120includes the multi-class classifier neural network122.

The training system110can train the neural network122using class weights that are different for one or more pairs, e.g., each pair, of predicted class and ground truth class. Predicted class may refer to the class that the neural network122classifies for the image, and the ground truth class may refer to the classes specified for images by training data which may be assumed to be always correct. The use of different weight pairs for the predicted class and ground truth class, used to update the neural network122using data from a prediction by the neural network122in a training iteration, can better correct the neural network122for incorrect predictions compared to other systems. In some implementations, the class weights may be in the form of an information gain loss matrix G that specifies classifications differently for pairs, e.g., each pair, of predicted class and ground truth class.

To train the neural network122, the training system110can compute an information gain loss E. The information gain loss E may be computed using equation (1), below.

In equation (1), G is the information gain matrix, and Glndenotes row lnof G that is the weight vector for the ground truth label lnof the nthimage. In a traditional loss function, e.g., multinomial logistic loss, G=Identity is the equivalent. However, to improve training of the neural network116, the training system110can use the information gain matrix G as described in more detail below.

In some implementations, the information gain matrix G can have, for a row lnfor a particular ground truth label, non-negative values that sum to one. For instance, the information gain matrix G can have a row lnthat includes at least one ground truth label that has a non-negative value other than one or zero.

Further, the information gain matrix G can have, for a row lnthat belongs to a particular final classification type, negative values for the locations indexed by the classes in the other final classification type. For instance, the image classifier114can have neural network116classifications and binary classifier layer118final classifications. The final classifications can be object of interest or not an object of interest, e.g., non-object of interest.

pnis the output vector from the neural network for the nthimage that includes, for each class k, a probability that the nthimage depicts an object of the corresponding class, and pn,kis the probability value determined by the neural network that the nthimage depicts an object of class k. In some examples, the probability values of each output vector pnfrom the neural network can sum to one, e.g., the probability values of each output vector pncan always sum to one.

If an element of G is <0, e.g., is negative, the training system110can apply a higher penalty on the loss of the neural network116incorrectly predicting a class, compared to logistic loss when the neural network116incorrectly predicts a class when an element of G is >=0, e.g., non-negative. The use of negative and non-negative values in the information gain matrix G can enable the training system110to train the neural network116more quickly than other training systems.

Table 1, below, depicts an example of an information gain loss matrix G. In Table 1, the class weight for a ground truth of Human and a prediction of Background, e.g., G[Human, Bg], is −0.7, thereby giving a high penalty when the neural network122classifies an image that depicts a human as a background. The class weight for a ground truth of Background and a prediction of Human, e.g., G[Bg, Human], is −0.1. This penalty for classifying a background sample as Human is more, e.g., instead of a loss of 0, but less than the higher penalty for incorrectly classifying an object of interest, e.g., a human.

The training system110can use non-negative class weights for classification pairs for which the output by the binary classifier layer124is the same, e.g., to allow some misclassification among non-object classes. For instance, the class weight for a ground truth of Background and a prediction of Spider Web, e.g., G[Bg, Sp], is a positive value: 0.5. Similarly, the class weight for a ground truth of Spider Web and a prediction of Background, e.g., G[Sp, Bg], is 0.5. The class weights for the classification pairs that are all objects of interest, e.g., animal140, vehicle142, and human144, can have non-negative values, e.g., zero.

In some examples, the information gain weight Glnfor a ground truth label ln, e.g., which is a vector, the non-negative values in the ground truth label that correspond to the same overall classification, e.g., object of interest or non-object of interest, can sum to one. For instance, for the example shown in table 1 above, both Background (BG) and Spider/web (Sp) are non-objects of interest. In the above information gain matrix G, the ground truth label GBgfor Background includes a value of 0.5 for Bg (background) and a value of 0.5 for Sp (spider/web) and the sum of these two values is one.

Similarly, for the example shown above, human (H), animal (A), and vehicle (C) are all objects of interest. For the ground truth label GHfor Human, there is a value of 1.0 for H and two values of 0.0 for A and C, which sum to one.

In the information gain matrix G, each ground truth vector includes a highest value. The highest value is for the class to which the corresponding vector belongs. For instance, for the ground truth vector GBg, the highest value is 0.5 and is in the location indexed by the prediction class Bg. Although this value can also be in another location, e.g., indexed by prediction class Sp, the highest value is at least in the location indexed by the corresponding class. In another example, for the ground truth vector GH, the highest value of 1.0 is in the location indexed by the prediction class H.

FIG.3is a block diagram of an example process300for detecting objects. Briefly, and as will be described in more detail below, the process300includes providing a training image that depicts an object to a neural network being trained to detect objects of interest (310), receiving, from the neural network, an indication that the object is a first class of object of non-interest (320), determining that the object is a second class of object of non-interest and was incorrectly classified by the neural network as the first class of object of non-interest (330), and weighting the neural network towards correctly classifying the object as the second class of object of non-interest (340).

The process300includes providing, to a neural network being trained to detect objects of interest, a training image that depicts an object (310). For example, the training engine112may provide an image of a spider web to the neural network116when the neural network116is partially trained. The neural network116can be partially trained to classify an image into one of the following classes: showing a spider web, showing background, showing an animal, showing a human, or showing a vehicle, where animals, humans, and vehicles are considered objects of interest and background and spider webs are considered objects of non-interest or otherwise not depicting an object of interest.

The process300includes receiving, from the neural network, an indication that the object is the first class of object of non-interest (320). For example, the training engine112may receive an indication from the neural network116that the image of the spider web is classified as showing background. The first class of object of non-interest can be background, e.g., the classification applied to the object by the neural network. The second class of object of non-interest can be spider web, e.g., the ground truth classification that the neural network should have predicted for the object.

The process300includes determining that the object is the second class of object of non-interest and was incorrectly classified by the neural network as the first class of object of non-interest (330). For example, the training engine112may receive the indication that the image was classified by the neural network116as showing background, e.g., the first class of object of non-interest. The training engine112can determine from an indication in training data that the ground truth class from the image is spider web, e.g., the second class of object of non-interest. The training engine112can determine that the indication of background and ground truth class of spider web do not match, and, in response, determine that the object is the second class of object of non-interest and was incorrectly classified as the first class of object of non-interest.

The process300includes weighting the neural network towards correctly classifying the object as the second class of object of non-interest (340). For example, in response to the neural network116incorrectly classifying the spider web as background, the training engine112may use a class weight value of −0.5 with a loss function that is used to further train the neural network116. The class weight value used with the loss function reinforces the neural network for future classification of the object, and similar objects, as a spider web instead of as the background.

FIG.4is a flow diagram of a process400for training an image classifier. For example, the process400can be used by the training system110, the training engine112, or both, from the environment100.

A training system provides, to a neural network of an image classifier that is trained to detect objects of the two or more classification types, a feature vector for a respective training image (402). For instance, the training system can generate the feature vector of the respective training image, e.g., using any appropriate feature vector generation process. In some examples, the training system can access a database that includes the feature vector for the training image.

The training system can perform one or more steps in the process, e.g., steps402through408, multiple times. For instance, the training system can perform step402for each of multiple different training images. Each of the training images can be associated with a class, e.g., a classification type, from two or more classes, e.g., classification types.

The multiple different training images can include at least one image for each class. For instance, when there are three classes, the multiple different training images can include at least three images, a first image for a first class, a second image for a second class, and a third image for a third class. During a training process, the training system is likely to use data for a larger number of training images, e.g., hundreds or thousands of training images.

The training system receives, from the neural network, an output vector that indicates, for each of the two or more classification types, a likelihood that the respective training image depicts an object of the corresponding classification type (404). The values in the output vector can sum to one. In some examples, the output vector can be a one-hot vector, e.g., that includes only one value of one.

For example, when there are three classification types, the output vector would include three values. When there are five classification types, the output vector would include five values.

The training system accesses, from two or more ground truth vectors each for one of the two or more classification types, a ground truth vector for the classification type of an object depicted in the respective training image (406). Each of the classification types has a corresponding ground truth vector. The ground truth vector can be part of a matrix, e.g., an information gain matrix G. Each vector can be a row in the matrix.

For example, the training system can access, for the training image, the ground truth vector that corresponds to the training image. The ground truth vector can represent the classification type, e.g., the ground truth label, to which the training image belongs. For instance, the training image can depict a spider web and have a ground truth label of “spider web”. The training system can use the ground truth label for the training image to access a database and determine the ground truth vector for the training image. Based on table 1, above, the training system can determine a ground truth vector of “[0.5−0.1−0.1−0.1 0.5]” for the training image.

Each ground truth vector can include a value for each of the two or more classification types. For instance, based on the above example, the ground truth vector can have a first value of “0.5” for the classification type “background,” a first value of “−0.1” for the classification type “human,” a second value of “−0.1” for the classification type “animal,” a third value of “−0.1” for the classification type “vehicle,” and a second value of “0.5” for the classification type “spider/web”.

Each of the two or more classification types can be a primary classification for a secondary or final classification. For instance, each of the classification types can be for a first final classification type of “object of interest” or a second final classification type of “not an object of interest” or “non-object of interest.”

A ground truth vector for a particular final classification includes non-negative vector values for the other classification types of the same final classification and negative vector values for the other classification types that have a different final classification. For instance, in the above example for the ground truth vector for background, the ground truth vector includes two non-negative values of “0.5” for the classification types of background and spider/web which all have the same final classification, e.g., not an object of interest. Further, the ground truth vector for background includes three negative values of “−0.1” for the classification types human, animal, and vehicle that all have a different final classification, e.g., object of interest, from the final classification for background, e.g., not an object of interest.

The training system can have three or more classification types. Two of the three or more classification types can be for the same final classification type. Two of the three or more classification types can have a final classification type of not an object of interest, e.g., non-object of interest.

The training system can have five or more classification types. In these examples, the training system can have two classification types with a final classification type of not an object of interest and three classification types with a final classification type of object of interest.

The training system adjusts one or more weights in the neural network using a combination of the output vector and the ground truth vector for the classification type of the objected depicted in the respective training image (408). The training system can use any appropriate process to combine the output vector and the ground truth vector. For instance, the training system can sum the two vectors, multiply the two vectors, divide the two vectors, or subtract one vector from the other. In some examples, the training system can multiply the two vectors and add the weighted output vector to get a training value.

The training system can combine, for each of the values in the ground truth vector, the respective value from the ground truth vector with a corresponding value from the output vector to generate combined values. In some examples, the training system can multiply corresponding values in the output vector by the corresponding values in the ground truth vector.

The training system can use the combined values to generate a training value. For instance, the training system can add all of the combined values to generate the training value. The training value can be the information gain loss E, described in more detail above.

The training system can adjust the one or more weights in the neural network using the training value. The training system can use backward propagation to adjust the one or more weights. In some examples, the training system can use the information gain loss E to adjust the one or more weights in the neural network.

The training system stores, in a memory, the image classifier that includes the neural network for use by a camera to classify objects detected in one or more images captured by the camera (410). The training system can create the image classifier by combining the neural network with a binary classifier layer to generate a binary neural network. The binary neural network is trained to receive a feature vector for an image as input and output a value that indicates a final classification type for the image, e.g., whether an object depicted in the image is an object of interest or is not an object of interest. The binary neural network contrasts with the neural network used during training that can be a multi-class classifier.

The training system can store the image classifier in the memory. For instance, the training system can store the image classifier in a memory of a server, or another computer, for use by a camera to classify objects detected in one or more images captured by the camera.

In some implementations, the training system or another system can provide the image classifier, that includes the neural network and the binary classifier layer, to a camera. The system can provide the image classifier to the camera for use by the camera classifying objects detected in one or more images captured by the camera.

The order of steps in the process400described above is illustrative only, and training a model to reduce false camera detections can be performed in different orders. For example, the training system can access the ground truth vector, e.g., perform step406, and then provide the feature vector, e.g., perform step402, receive the likelihood, e.g., perform step404, or both.

In some implementations, the process400can include additional steps, fewer steps, or some of the steps can be divided into multiple steps. For example, the process400can include steps402through408without step410.

In some implementations, the training system can determine whether to add an additional class, e.g., classification type. For instance, the training system can include two sets of labeled training data, e.g., training images, training vectors, or both. The training system can use a first set from the two sets to train the neural network. The training system can use a second set from the two sets to test an accuracy of the neural network.

When the training system determines that the accuracy of the neural network satisfies an accuracy threshold, the training system can determine to stop training the neural network and store the neural network in memory. For instance, the training system can combine the neural network with the binary classification layer to create the image classifier and store the image classifier in memory.

When the training system determines that the accuracy of the neural network does not satisfy the accuracy threshold, the training system can determine whether to create a new classification type for the neural network. For example, the training system can determine whether the neural network had at least a threshold false positive rate for background images, one of the classification types for objects of interest, or both.

If so, the training system can determine whether an amount of the training images that the neural network incorrectly classified that are of the same type, e.g., are all of another classification type for which the neural network was not separately trained, satisfies a threshold amount. The training system can make this determination using detailed labels for training images, which labels are stored in a database. The training system can make this determination using input from an administrator. The training system can use any appropriate process to make this determination.

When the training system determines that the amount of training images that the neural network incorrectly classified that are of the same type satisfies the threshold amount, e.g., quantity or percentage, the training system can determine to create another classification type depending on what is depicted in incorrectly classified images of the same type. The new classification type can be for water, plants, or boats (on a trailer). This can include the training system creating a ground truth vector for the new classification type. The training system can update one or more existing ground truth vectors to include data, e.g., a value, for the new classification type. The training system can update an information gain matrix to include data for the new classification type. The training system can update the binary classification layer to output a particular final classification, e.g., not an object of interest or object of interest, for outputs from the neural network that have a highest value for the new classification type.

When the accuracy of the neural network satisfies the accuracy threshold or that the amount of training images that the neural network incorrectly classified that are of the same type does not satisfy the threshold amount, the training system can determine to skip adding a new classification type to the neural network. For instance, the training system can determine to stop training and store the neural network in memory. The training system can create the image classifier using the neural network.

The training system can determine one or more values for the ground truth vector for the new classification type using a quantity of training images for the new classification type. For instance, when the new classification type is for another object of interest, the new classification type can determine a ground truth value for the background index using the quantity of training images. When the quantity of training images does not satisfy a quantity threshold, the training system can select a more negative, e.g., further from zero, value than the training system would select had the training system determined that the quantity of training images satisfied the quantity threshold.

In some examples, the absolute values for the ground truth vectors can be between zero and one, inclusive. For instance, the values can include 0.0, 0.1, 0.2, 0.5, and 1.0.

In some implementations, when a camera uses the image classifier, the image classifier is one of multiple analysis processes used. For instance, the camera can first use a motion detector to determine whether there is motion in the camera's field of view. If so, the camera can analyze one or more images of an area in which motion was detected using the image classifier. When the camera determines that the analyzed image do not depict an object of interest, the camera can stop its analysis based on the detected motion.

When the camera determines that the analyzed image depicts an object of interest using the image classifier, the camera can send a message to the monitoring server. The message can include the one or more images, other data about the detected motion, or both. The other data can include a time of day, location on a property, or both. The monitoring server can then analyze the received data and determine an action to perform, such as turning on a light.

FIG.5is a diagram illustrating an example of a home monitoring system500. The home monitoring system500includes a network505, a control unit510, one or more user devices540and550, a monitoring server560, and a central alarm station server570. In some examples, the network505facilitates communications between the control unit510, the one or more user devices540and550, the monitoring server560, and the central alarm station server570.

The network505is configured to enable exchange of electronic communications between devices connected to the network505. For example, the network505may be configured to enable exchange of electronic communications between the control unit510, the one or more user devices540and550, the monitoring server560, and the central alarm station server570. The network505may 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. Network505may include multiple networks or subnetworks, each of which may include, for example, a wired or wireless data pathway. The network505may 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 network505may 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 network505may include one or more networks that include wireless data channels and wireless voice channels. The network505may be a wireless network, a broadband network, or a combination of networks including a wireless network and a broadband network.

The control unit510includes a controller512and a network module514. The controller512is configured to control a control unit monitoring system (e.g., a control unit system) that includes the control unit510. In some examples, the controller512may 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 controller512may 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 controller512may be configured to control operation of the network module514included in the control unit510.

The network module514is a communication device configured to exchange communications over the network505. The network module514may be a wireless communication module configured to exchange wireless communications over the network505. For example, the network module514may be a wireless communication device configured to exchange communications over a wireless data channel and a wireless voice channel. In this example, the network module514may 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, a 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 module514also may be a wired communication module configured to exchange communications over the network505using a wired connection. For instance, the network module514may be a modem, a network interface card, or another type of network interface device. The network module514may be an Ethernet network card configured to enable the control unit510to communicate over a local area network and/or the Internet. The network module514also 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 unit510includes one or more sensors. For example, the monitoring system500may include multiple sensors520. The sensors520may include a lock sensor, a contact sensor, a motion sensor, or any other type of sensor included in a control unit system. The sensors520also 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 sensors520further 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 sensors520can also include a radio-frequency identification (RFID) sensor that identifies a particular article that includes a pre-assigned RFID tag.

The control unit510communicates with the home automation controls522and a camera530to perform monitoring. The home automation controls522are connected to one or more devices that enable automation of actions in the home. For instance, the home automation controls522may be connected to one or more lighting systems and may be configured to control operation of the one or more lighting systems. Also, the home automation controls522may 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 controls522may 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 controls522may include multiple modules that are each specific to the type of device being controlled in an automated manner. The home automation controls522may control the one or more devices based on commands received from the control unit510. For instance, the home automation controls522may cause a lighting system to illuminate an area to provide a better image of the area when captured by a camera530.

The camera530may be a video/photographic camera or other type of optical sensing device configured to capture images. For instance, the camera530may be configured to capture images of an area within a building or home monitored by the control unit510. The camera530may be configured to capture single, static images of the area or 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) or both. The camera530may be controlled based on commands received from the control unit510.

The camera530may be triggered by several different types of techniques. For instance, a Passive Infra-Red (PIR) motion sensor may be built into the camera530and used to trigger the camera530to capture one or more images when motion is detected. The camera530also may include a microwave motion sensor built into the camera and used to trigger the camera530to capture one or more images when motion is detected. The camera530may have a “normally open” or “normally closed” digital input that can trigger capture of one or more images when external sensors (e.g., the sensors520, PIR, door/window, etc.) detect motion or other events. In some implementations, the camera530receives a command to capture an image when external devices detect motion or another potential alarm event. The camera530may receive the command from the controller512or directly from one of the sensors520.

In some examples, the camera530triggers integrated or external illuminators (e.g., Infra-Red, Z-wave controlled “white” lights, lights controlled by the home automation controls522, 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 camera530may 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 camera530may enter a low-power mode when not capturing images. In this case, the camera530may wake periodically to check for inbound messages from the controller512. The camera530may be powered by internal, replaceable batteries, e.g., if located remotely from the control unit510. The camera530may employ a small solar cell to recharge the battery when light is available. The camera530may be powered by the controller's512power supply if the camera530is co-located with the controller512.

In some implementations, the camera530communicates directly with the monitoring server560over the Internet. In these implementations, image data captured by the camera530does not pass through the control unit510and the camera530receives commands related to operation from the monitoring server560.

The system500also includes thermostat534to perform dynamic environmental control at the home. The thermostat534is configured to monitor temperature and/or energy consumption of an HVAC system associated with the thermostat534, and is further configured to provide control of environmental (e.g., temperature) settings. In some implementations, the thermostat534can 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 thermostat534can directly measure energy consumption of the HVAC system associated with the thermostat, or can estimate energy consumption of the HVAC system associated with the thermostat534, for example, based on detected usage of one or more components of the HVAC system associated with the thermostat534. The thermostat534can communicate temperature and/or energy monitoring information to or from the control unit510and can control the environmental (e.g., temperature) settings based on commands received from the control unit510.

In some implementations, the thermostat534is a dynamically programmable thermostat and can be integrated with the control unit510. For example, the dynamically programmable thermostat534can include the control unit510, e.g., as an internal component to the dynamically programmable thermostat534. In addition, the control unit510can be a gateway device that communicates with the dynamically programmable thermostat534. In some implementations, the thermostat534is controlled via one or more home automation controls522.

A module537is 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 module537is 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 module537can communicate energy monitoring information and the state of the HVAC system components to the thermostat534and can control the one or more components of the HVAC system based on commands received from the thermostat534.

In some examples, the system500further includes one or more robotic devices590. The robotic devices590may be any type of robots that are capable of moving and taking actions that assist in home monitoring. For example, the robotic devices590may 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 also 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 devices590may be robotic devices590that are intended for other purposes and merely associated with the system500for use in appropriate circumstances. For instance, a robotic vacuum cleaner device may be associated with the monitoring system500as one of the robotic devices590and may be controlled to take action responsive to monitoring system events.

In some examples, the robotic devices590automatically navigate within a home. In these examples, the robotic devices590include sensors and control processors that guide movement of the robotic devices590within the home. For instance, the robotic devices590may 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 devices590may include control processors that process output from the various sensors and control the robotic devices590to 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 devices590in a manner that avoids the walls and other obstacles.

In addition, the robotic devices590may store data that describes attributes of the home. For instance, the robotic devices590may store a floorplan and/or a three-dimensional model of the home that enables the robotic devices590to navigate the home. During initial configuration, the robotic devices590may 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 devices590also may include learning of one or more navigation patterns in which a user provides input to control the robotic devices590to 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 devices590may learn and store the navigation patterns such that the robotic devices590may automatically repeat the specific navigation actions upon a later request.

In some examples, the robotic devices590may include data capture and recording devices. In these examples, the robotic devices590may 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 sensor 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 devices590to 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 devices590may include output devices. In these implementations, the robotic devices590may include one or more displays, one or more speakers, and/or any type of output devices that allow the robotic devices590to communicate information to a nearby user.

The robotic devices590also may include a communication module that enables the robotic devices590to communicate with the control unit510, each other, and/or other devices. The communication module may be a wireless communication module that allows the robotic devices590to communicate wirelessly. For instance, the communication module may be a Wi-Fi module that enables the robotic devices590to 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 devices590to communicate directly with the control unit510. Other types of short-range wireless communication protocols, such as Bluetooth, Bluetooth LE, Z-wave, Zigbee, etc., may be used to allow the robotic devices590to communicate with other devices in the home. In some implementations, the robotic devices590may communicate with each other or with other devices of the system500through the network505.

The robotic devices590further may include processor and storage capabilities. The robotic devices590may include any suitable processing devices that enable the robotic devices590to operate applications and perform the actions described throughout this disclosure. In addition, the robotic devices590may include solid-state electronic storage that enables the robotic devices590to store applications, configuration data, collected sensor data, and/or any other type of information available to the robotic devices590.

The robotic devices590are 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 devices590may be configured to navigate to the charging stations after completion of tasks needed to be performed for the home monitoring system500. For instance, after completion of a monitoring operation or upon instruction by the control unit510, the robotic devices590may be configured to automatically fly to and land on one of the charging stations. In this regard, the robotic devices590may automatically maintain a fully charged battery in a state in which the robotic devices590are ready for use by the home monitoring system500.

The charging stations may be contact based charging stations and/or wireless charging stations. For contact based charging stations, the robotic devices590may have readily accessible points of contact that the robotic devices590are 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 devices590may charge through a wireless exchange of power. In these cases, the robotic devices590need 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 devices590landing at a wireless charging station, the wireless charging station outputs a wireless signal that the robotic devices590receive and convert to a power signal that charges a battery maintained on the robotic devices590.

In some implementations, each of the robotic devices590has a corresponding and assigned charging station such that the number of robotic devices590equals the number of charging stations. In these implementations, the robotic devices590always 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 devices590may share charging stations. For instance, the robotic devices590may use one or more community charging stations that are capable of charging multiple robotic devices590. The community charging station may be configured to charge multiple robotic devices590in parallel. The community charging station may be configured to charge multiple robotic devices590in serial such that the multiple robotic devices590take 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 devices590.

Also, the charging stations may not be assigned to specific robotic devices590and may be capable of charging any of the robotic devices590. In this regard, the robotic devices590may use any suitable, unoccupied charging station when not in use. For instance, when one of the robotic devices590has completed an operation or is in need of battery charge, the control unit510references 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 system500further includes one or more integrated security devices580. 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 units510may provide one or more alerts to the one or more integrated security input/output devices580. Additionally, the one or more control units510may receive sensor data from the sensors520and determine whether to provide an alert to the one or more integrated security input/output devices580.

The sensors520, the home automation controls522, the camera530, the thermostat534, and the integrated security devices580may communicate with the controller512over communication links524,526,528,532,538, and584. The communication links524,526,528,532,538, and584may be a wired or wireless data pathway configured to transmit signals from the sensors520, the home automation controls522, the camera530, the thermostat534, and the integrated security devices580to the controller512. The sensors520, the home automation controls522, the camera530, the thermostat534, and the integrated security devices580may continuously transmit sensed values to the controller512, periodically transmit sensed values to the controller512, or transmit sensed values to the controller512in response to a change in a sensed value.

The communication links524,526,528,532,538, and584may include a local network. The sensors520, the home automation controls522, the camera530, the thermostat534, and the integrated security devices580, and the controller512may 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 (CAT5) 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 server560is an electronic device configured to provide monitoring services by exchanging electronic communications with the control unit510, the one or more user devices540and550, and the central alarm station server570over the network505. For example, the monitoring server560may be configured to monitor events (e.g., alarm events) generated by the control unit510. In this example, the monitoring server560may exchange electronic communications with the network module514included in the control unit510to receive information regarding events (e.g., alerts) detected by the control unit510. The monitoring server560also may receive information regarding events (e.g., alerts) from the one or more user devices540and550.

In some examples, the monitoring server560may route alert data received from the network module514or the one or more user devices540and550to the central alarm station server570. For example, the monitoring server560may transmit the alert data to the central alarm station server570over the network505.

The monitoring server560may store sensor and image data received from the monitoring system500and perform analysis of sensor and image data received from the monitoring system500. Based on the analysis, the monitoring server560may communicate with and control aspects of the control unit510or the one or more user devices540and550.

The monitoring server560may provide various monitoring services to the system500. For example, the monitoring server560may analyze the sensor, image, and other data to determine an activity pattern of a resident of the home monitored by the system500. In some implementations, the monitoring server560may analyze the data for alarm conditions or may determine and perform actions at the home by issuing commands to one or more of the controls522, possibly through the control unit510.

The central alarm station server570is an electronic device configured to provide alarm monitoring service by exchanging communications with the control unit510, the one or more mobile devices540and550, and the monitoring server560over the network505. For example, the central alarm station server570may be configured to monitor alerting events generated by the control unit510. In this example, the central alarm station server570may exchange communications with the network module514included in the control unit510to receive information regarding alerting events detected by the control unit510. The central alarm station server570also may receive information regarding alerting events from the one or more mobile devices540and550and/or the monitoring server560.

The central alarm station server570is connected to multiple terminals572and574. The terminals572and574may be used by operators to process alerting events. For example, the central alarm station server570may route alerting data to the terminals572and574to enable an operator to process the alerting data. The terminals572and574may 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 server570and render a display of information based on the alerting data. For instance, the controller512may control the network module514to transmit, to the central alarm station server570, alerting data indicating that a sensor520detected motion from a motion sensor via the sensors520. The central alarm station server570may receive the alerting data and route the alerting data to the terminal572for processing by an operator associated with the terminal572. The terminal572may 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 terminals572and574may be mobile devices or devices designed for a specific function. AlthoughFIG.5illustrates two terminals for brevity, actual implementations may include more (and, perhaps, many more) terminals.

The one or more authorized user devices540and550are devices that host and display user interfaces. For instance, the user device540is a mobile device that hosts or runs one or more native applications (e.g., the smart home application542). The user device540may be a cellular phone or a non-cellular locally networked device with a display. The user device540may 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 device540may 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 device540includes a smart home application542. The smart home application542refers to a software/firmware program running on the corresponding mobile device that enables the user interface and features described throughout. The user device540may load or install the smart home application542based on data received over a network or data received from local media. The smart home application542runs on mobile devices platforms, such as iPhone, iPod touch, Blackberry, Google Android, Windows Mobile, etc. The smart home application542enables the user device540to receive and process image and sensor data from the monitoring system.

The user device550may 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 server560and/or the control unit510over the network505. The user device550may be configured to display a smart home user interface552that is generated by the user device550or generated by the monitoring server560. For example, the user device550may be configured to display a user interface (e.g., a web page) provided by the monitoring server560that enables a user to perceive images captured by the camera530and/or reports related to the monitoring system. AlthoughFIG.5illustrates 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 devices540and550communicate with and receive monitoring system data from the control unit510using the communication link538. For instance, the one or more user devices540and550may communicate with the control unit510using 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 devices540and550to local security and automation equipment. The one or more user devices540and550may 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 network505with a remote server (e.g., the monitoring server560) may be significantly slower.

Although the one or more user devices540and550are shown as communicating with the control unit510, the one or more user devices540and550may communicate directly with the sensors and other devices controlled by the control unit510. In some implementations, the one or more user devices540and550replace the control unit510and perform the functions of the control unit510for local monitoring and long range/offsite communication.

In other implementations, the one or more user devices540and550receive monitoring system data captured by the control unit510through the network505. The one or more user devices540,550may receive the data from the control unit510through the network505or the monitoring server560may relay data received from the control unit510to the one or more user devices540and550through the network505. In this regard, the monitoring server560may facilitate communication between the one or more user devices540and550and the monitoring system.

In some implementations, the one or more user devices540and550may be configured to switch whether the one or more user devices540and550communicate with the control unit510directly (e.g., through link538) or through the monitoring server560(e.g., through network505) based on a location of the one or more user devices540and550. For instance, when the one or more user devices540and550are located close to the control unit510and in range to communicate directly with the control unit510, the one or more user devices540and550use direct communication. When the one or more user devices540and550are located far from the control unit510and not in range to communicate directly with the control unit510, the one or more user devices540and550use communication through the monitoring server560.

Although the one or more user devices540and550are shown as being connected to the network505, in some implementations, the one or more user devices540and550are not connected to the network505. In these implementations, the one or more user devices540and550communicate 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 system500provides end users with access to images captured by the camera530to aid in decision-making. The system500may transmit the images captured by the camera530over a wireless WAN network to the user devices540and550. Because transmission over a wireless WAN network may be relatively expensive, the system500can 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 system500and other events sensed by the monitoring system500may be used to enable/disable video/image recording devices (e.g., the camera530). In these implementations, the camera530may 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 camera530may 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 camera530, or motion in the area within the field of view of the camera530. In other implementations, the camera530may capture images continuously, but the captured images may be stored or transmitted over a network when needed.