Patent Publication Number: US-10769799-B2

Title: Foreground detection

Description:
BACKGROUND 
     Vehicles can be equipped to operate in both autonomous and occupant piloted mode. Vehicles can be equipped with computing devices, networks, sensors and controllers to acquire information regarding the vehicle&#39;s environment and to operate the vehicle based on the information. Safe and comfortable operation of the vehicle can depend upon acquiring accurate and timely information regarding the vehicle&#39;s environment. Vehicle sensors can provide data concerning routes to be traveled and objects to be avoided in the vehicle&#39;s environment. Safe and efficient operation of the vehicle can depend upon acquiring accurate and timely information regarding routes and objects in a vehicle&#39;s environment while the vehicle is being operated on a roadway. 
    
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         FIG. 1  is a block diagram of an example vehicle. 
         FIG. 2  is a diagram of an example traffic scene including a stationary camera. 
         FIG. 3  is a diagram of an example image of a traffic scene acquired by a stationary camera 
         FIG. 4  is a diagram of an example image including determined moving objects. 
         FIG. 5  is a diagram of an example cognitive map. 
         FIG. 6  is a flowchart diagram of an example process to operate a vehicle based on moving objects. 
         FIG. 7  is a flowchart diagram of an example process to determine foreground objects. 
     
    
    
     DETAILED DESCRIPTION 
     Vehicles can be equipped to operate in both autonomous and occupant piloted mode. By a semi- or fully-autonomous mode, we mean a mode of operation wherein a vehicle can be piloted by a computing device as part of a vehicle information system having sensors and controllers. The vehicle can be occupied or unoccupied, but in either case the vehicle can be piloted without assistance of an occupant. For purposes of this disclosure, an autonomous mode is defined as one in which each of vehicle propulsion (e.g., via a powertrain including an internal combustion engine and/or electric motor), braking, and steering are controlled by one or more vehicle computers; in a semi-autonomous mode the vehicle computer(s) control(s) one or two of vehicle propulsion, braking, and steering. In a non-autonomous vehicle, none of these are controlled by a computer. 
     A computing device in a vehicle can be programmed to acquire data regarding the external environment of a vehicle and to use the data to determine trajectories to be used to operate a vehicle in autonomous or semi-autonomous mode, for example, wherein the computing device can provide information to controllers to operate vehicle on a roadway in traffic including other vehicles. Based on sensor data, a computing device can determine moving objects including vehicles and pedestrians in the vicinity of a vehicle and operate a vehicle based on the moving objects. For example, a computing device can detect and identify moving objects in the vicinity of a vehicle and, based on detecting and identifying moving objects at a plurality of time periods, determine a velocity, including speed and direction, for the moving objects. Thus, the computing device enjoys improved accuracy in analyzing sensor, e.g., image, data, and in identifying and determining trajectories of, moving objects. 
     Disclosed herein is a method, including receiving an image including foreground pixels determined based on determining an eccentricity ε k  for a sequence of images acquired by a stationary sensor, determining moving objects in the image based on the foreground pixels, and operating a vehicle based on the moving objects in the image. Eccentricity ε k  can be determined based on determining a mean μ k  for pixels of the sequence of images, based on a previous mean μ k-1  according to equation μ k =(1−α)μ k-1 +αx k  where α is an empirically determined constant. The eccentricity ε k  can be determined based on determining a variance σ k   2  for pixels of the sequence of images, based on a previous variance σ k-1   2  and a mean μ k  according to equation 
               σ   k   2     =         (     1   -   α     )     ⁢     σ     k   -   1     2       +             α   ⁡     (       x   k     -     μ   k       )       T     ⁢     (       x   k     -     μ   k       )         1   -   α       .             
The eccentricity ε k  can be determined based on mean μ k  and variance σ k   2  according to equation
 
     
       
         
           
             
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     The foreground pixels can be determined by comparing ε k  to α times an empirically determined constant. Moving objects in the image can be determined based on determining connected regions of foreground pixels with empirically determined minimum and maximum areas. The connected regions of foreground pixels can be determined based on having similar eccentricity ε k . The stationary sensor can be a video camera included in a traffic infrastructure system. The vehicle can receive the image including foreground pixels from the traffic infrastructure system via a network based on a location of the vehicle. The moving objects can be projected onto a cognitive map based on the location of the video camera. The cognitive map can be determined based on the location of the vehicle, map data, vehicle sensor data and the moving objects. The vehicle can be operated based on a path polynomial based on the cognitive map. The video camera can be fixtured to acquire an unchanging field of view. The vehicle can be operated by controlling vehicle steering, braking, and powertrain. 
     Further disclosed is a computer readable medium, storing program instructions for executing some or all of the above method steps. Further disclosed is a computer programmed for executing some or all of the above method steps, including a computer apparatus, programmed to receive an image including foreground pixels determined based on determining an eccentricity ε k  for a sequence of images acquired by a stationary sensor, determining moving objects in the image based on the foreground pixels, and operating a vehicle based on the moving objects in the image. Eccentricity ε k  can be determined based on determining a mean μ k  for pixels of the sequence of images, based on a previous mean μ k-1  according to equation μ k =(1−α)μ k-1 +αx k  where α is an empirically determined constant. The eccentricity ε k  can be determined based on determining a variance σ k   2  for pixels of the sequence of images, based on a previous variance σ k-1   2  and mean μ k  according to equation 
               σ   k   2     =         (     1   -   α     )     ⁢     σ     k   -   1     2       +             α   ⁡     (       x   k     -     μ   k       )       T     ⁢     (       x   k     -     μ   k       )         1   -   α       .             
The eccentricity ε k  can be determined based on mean μ k  and variance σ k   2  according to equation
 
     
       
         
           
             
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     The computer apparatus can be further programmed to determine the foreground pixels by comparing ε k  to α times an empirically determined constant. Moving objects in the image can be determined based on determining connected regions of foreground pixels with empirically determined minimum and maximum areas. The connected regions of foreground pixels can be determined based on having similar eccentricity ε k . The stationary sensor can be a red, green, blue (RGB) color video camera included in a traffic infrastructure system, for example. The vehicle can receive the image including foreground pixels from the traffic infrastructure system via a network based on a location of the vehicle. The moving objects can be projected onto a cognitive map based on the location of the video camera. The cognitive map can be determined based on the location of the vehicle, map data, vehicle sensor data and the moving objects. The vehicle can be operated based on a path polynomial based on the cognitive map. The video camera can be fixtured to acquire an unchanging field of view. The vehicle can be operated by controlling vehicle steering, braking, and powertrain. 
       FIG. 1  is a diagram of a traffic infrastructure system  100  that includes a vehicle  110  operable in autonomous (“autonomous” by itself in this disclosure means “fully autonomous”) and occupant piloted (also referred to as non-autonomous) mode. Vehicle  110  also includes one or more computing devices  115  for performing computations for piloting the vehicle  110  during autonomous operation. Computing devices  115  can receive information regarding the operation of the vehicle from sensors  116 . The computing device  115  may operate the vehicle  110  in an autonomous mode, a semi-autonomous mode, or a non-autonomous mode. For purposes of this disclosure, an autonomous mode is defined as one in which each of vehicle  110  propulsion, braking, and steering are controlled by the computing device; in a semi-autonomous mode the computing device  115  controls one or two of vehicle&#39;s  110  propulsion, braking, and steering; in a non-autonomous mode, a human operator controls the vehicle propulsion, braking, and steering. 
     The computing device  115  includes a processor and a memory such as are known. Further, the memory includes one or more forms of computer-readable media, and stores instructions executable by the processor for performing various operations, including as disclosed herein. For example, the computing device  115  may include programming to operate one or more of vehicle brakes, propulsion (e.g., control of acceleration in the vehicle  110  by controlling one or more of an internal combustion engine, electric motor, hybrid engine, etc.), steering, climate control, interior and/or exterior lights, etc., as well as to determine whether and when the computing device  115 , as opposed to a human operator, is to control such operations. 
     The computing device  115  may include or be communicatively coupled to, e.g., via a vehicle communications bus as described further below, more than one computing devices, e.g., controllers or the like included in the vehicle  110  for monitoring and/or controlling various vehicle components, e.g., a powertrain controller  112 , a brake controller  113 , a steering controller  114 , etc. The computing device  115  is generally arranged for communications on a vehicle communication network, e.g., including a bus in the vehicle  110  such as a controller area network (CAN) or the like; the vehicle  110  network can additionally or alternatively include wired or wireless communication mechanisms such as are known, e.g., Ethernet or other communication protocols. 
     Via the vehicle network, the computing device  115  may transmit messages to various devices in the vehicle and/or receive messages from the various devices, e.g., controllers, actuators, sensors, etc., including sensors  116 . Alternatively, or additionally, in cases where the computing device  115  actually comprises multiple devices, the vehicle communication network may be used for communications between devices represented as the computing device  115  in this disclosure. Further, as mentioned below, various controllers or sensing elements such as sensors  116  may provide data to the computing device  115  via the vehicle communication network. 
     In addition, the computing device  115  may be configured for communicating through a vehicle-to-infrastructure (V-to-I) interface  111  with a remote server computer  120 , e.g., a cloud server, via a network  130 , which, as described below, includes hardware, firmware, and software that permits computing device  115  to communicate with a remote server computer  120  via a network  130  such as wireless Internet (Wi-Fi) or cellular networks. V-to-I interface  111  may accordingly include processors, memory, transceivers, etc., configured to utilize various wired and/or wireless networking technologies, e.g., cellular, BLUETOOTH® and wired and/or wireless packet networks. Computing device  115  may be configured for communicating with other vehicles  110  through V-to-I interface  111  using vehicle-to-vehicle (V-to-V) networks, e.g., according to Dedicated Short Range Communications (DSRC) and/or the like, e.g., formed on an ad hoc basis among nearby vehicles  110  or formed through infrastructure-based networks. The computing device  115  also includes nonvolatile memory such as is known. Computing device  115  can log information by storing the information in nonvolatile memory for later retrieval and transmittal via the vehicle communication network and a vehicle to infrastructure (V-to-I) interface  111  to a server computer  120  or user mobile device  160 . 
     As already mentioned, generally included in instructions stored in the memory and executable by the processor of the computing device  115  is programming for operating one or more vehicle  110  components, e.g., braking, steering, propulsion, etc., without intervention of a human operator. Using data received in the computing device  115 , e.g., the sensor data from the sensors  116 , the server computer  120 , etc., the computing device  115  may make various determinations and/or control various vehicle  110  components and/or operations without a driver to operate the vehicle  110 . For example, the computing device  115  may include programming to regulate vehicle  110  operational behaviors (i.e., physical manifestations of vehicle  110  operation) such as speed, acceleration, deceleration, steering, etc., as well as tactical behaviors (i.e., control of operational behaviors typically in a manner intended to achieve safe and efficient traversal of a route) such as a distance between vehicles and/or amount of time between vehicles, lane-change, minimum gap between vehicles, left-turn-across-path minimum, time-to-arrival at a particular location and intersection (without signal) minimum time-to-arrival to cross the intersection. 
     Controllers, as that term is used herein, include computing devices that typically are programmed to control a specific vehicle subsystem. Examples include a powertrain controller  112 , a brake controller  113 , and a steering controller  114 . A controller may be an electronic control unit (ECU) such as is known, possibly including additional programming as described herein. The controllers may communicatively be connected to and receive instructions from the computing device  115  to actuate the subsystem according to the instructions. For example, the brake controller  113  may receive instructions from the computing device  115  to operate the brakes of the vehicle  110 . 
     The one or more controllers  112 ,  113 ,  114  for the vehicle  110  may include known electronic control units (ECUs) or the like including, as non-limiting examples, one or more powertrain controllers  112 , one or more brake controllers  113 , and one or more steering controllers  114 . Each of the controllers  112 ,  113 ,  114  may include respective processors and memories and one or more actuators. The controllers  112 ,  113 ,  114  may be programmed and connected to a vehicle  110  communications bus, such as a controller area network (CAN) bus or local interconnect network (LIN) bus, to receive instructions from the computer  115  and control actuators based on the instructions. 
     Sensors  116  may include a variety of devices known to provide data via the vehicle communications bus. For example, a radar fixed to a front bumper (not shown) of the vehicle  110  may provide a distance from the vehicle  110  to a next vehicle in front of the vehicle  110 , or a global positioning system (GPS) sensor disposed in the vehicle  110  may provide geographical coordinates of the vehicle  110 . The distance(s) provided by the radar and/or other sensors  116  and/or the geographical coordinates provided by the GPS sensor may be used by the computing device  115  to operate the vehicle  110  autonomously or semi-autonomously. 
     The vehicle  110  is generally a land-based vehicle  110  capable of autonomous and/or semi-autonomous operation and having three or more wheels, e.g., a passenger car, light truck, etc. The vehicle  110  includes one or more sensors  116 , the V-to-I interface  111 , the computing device  115  and one or more controllers  112 ,  113 ,  114 . The sensors  116  may collect data related to the vehicle  110  and the environment in which the vehicle  110  is operating. By way of example, and not limitation, sensors  116  may include, e.g., altimeters, cameras, LIDAR, radar, ultrasonic sensors, infrared sensors, pressure sensors, accelerometers, gyroscopes, temperature sensors, pressure sensors, hall sensors, optical sensors, voltage sensors, current sensors, mechanical sensors such as switches, etc. The sensors  116  may be used to sense the environment in which the vehicle  110  is operating, e.g., sensors  116  can detect phenomena such as weather conditions (precipitation, external ambient temperature, etc.), the grade of a road, the location of a road (e.g., using road edges, lane markings, etc.), or locations of target objects such as neighboring vehicles  110 . The sensors  116  may further be used to collect data including dynamic vehicle  110  data related to operations of the vehicle  110  such as velocity, yaw rate, steering angle, engine speed, brake pressure, oil pressure, the power level applied to controllers  112 ,  113 ,  114  in the vehicle  110 , connectivity between components, and accurate and timely performance of components of the vehicle  110 . 
       FIG. 2  is a diagram of an example image  200  of a traffic scene, rendered in black and white to comply with to comply with 37 C.F.R. § 1.84(a)(1). This example image  200  includes a roadway  202  and traffic objects  204 . Traffic objects can include vehicles, pedestrians, bicycles, animals or debris, etc. Image  200  also includes a stationary video camera  206 , which can be mounted on a mounting pole  208  or any other appropriate structure including traffic signals or buildings. Stationary video camera  206  has a lens  210  having a field of view  214 , represented by dotted lines, and an optical axis  212 , represented by a dashed line. The field of view  214  can be represented by a magnification of lens  210 , combined with an optical axis  212 , defined as a direction in 3D space located at a 3D location of an optical center of lens  210 . Optical axis  212  can represent the center in 3D space of field of view  214 . 
     Field of view  214  determines the portion of 3D space captured in an image, i.e., a digital image, by stationary video camera  206 , and thereby acquired as a video image by stationary video camera  206 . The 3D location and pose of stationary video camera  206 , where 3D location is defined by x, y, z coordinates with respect to latitude, longitude and altitude and pose is defined by ρ, ϕ, θ angles of rotation with respect to axes defined by latitude, longitude and altitude, can be determined empirically. Based on determining the 3D location and pose of stationary video camera  206 , the field of view  214  can be determined. 
     Because the field of view  214  of stationary video camera  206  does not change, real world 3D distances from stationary video camera  206  to real world background locations can be determined by physically measuring the real world 3D location of objects such as roadways represented in a video image acquired by stationary video camera  206 , for example. Other techniques for determining real world 3D locations of objects include photogrammetry, where a priori information regarding the size of an object can be used to determine the real world 3D location of the object in relation to the stationary video camera. Once the real world 3D locations of objects within the field of view of stationary video camera  206  are determined, the 3D locations can be assigned to pixel coordinates (i, j) in a video image, for example. The 3D location information can be stored at a computing device included in a traffic infrastructure system  100 . 
     Stationary video camera  206  can be part of the traffic infrastructure system  100 . A traffic infrastructure system as that term is used herein includes a network of computing devices and sensors that acquire data regarding vehicle traffic in areas that include roadways and portions of roadways and communicate with vehicles included in the vehicle traffic. A traffic infrastructure system  100  can include communications networks and computing devices that monitor and direct vehicle traffic over areas such as cities, neighborhoods, districts, or highways, for example. A traffic infrastructure system  100  can include sensors, like stationary video camera  206 , to record video data of traffic and process the video data and transmit it to computing devices in communication with many stationary video cameras  206 , for example, which can use the video data of traffic to determine traffic patterns and rates. Traffic infrastructure system  100  also can include wireless communications equipment that permits the traffic infrastructure system  100  to form ad-hoc networks with a vehicle  110  based on location in a geographic area defined by the traffic infrastructure system. A traffic infrastructure system  100  can include a plurality of transmitting and receiving stations, for example, and manage the ad-hoc networks in the same fashion as cellular telephone networks manage cell phone communications. For example, a traffic infrastructure system  100  can include communications from a server  120  with a vehicle  110  using V-to-I interface  111  whenever vehicle  110  was within range of traffic infrastructure system  100 , e.g., communications elements included in the network  130 , wherein “within range” is defined as the area within which vehicle  110  can receive a usable signal from a traffic infrastructure system  100  transceiver. 
       FIG. 3  is an example video image  300  acquired by a stationary video camera  206 , rendered in black and white to comply with Patent Office regulations. Video image  300  includes images of a roadway  302 , background objects  304 , including curbs, barrels and poles, etc., and foreground objects including traffic vehicles  306 ,  308 ,  310 ,  312 . 
     Determining foreground and background objects in a video image  300  can be used by a traffic infrastructure system  100  for a variety of tasks including traffic flow analysis, pedestrian tracking, and vehicle  110  operation, for example. Foreground and background objects in a video image  300  can be determined by acquiring and storing a first video image  300  at computing device  115 . A second video image  300  can be acquired and the first video image  300  subtracted from the second video image  300 . The subtracted result image contains zeros at pixel locations where the data did not change between the first and second video images  300  and non-zero values at pixel locations that did change. The non-zero values are caused by moving or foreground objects in the second video image  300  causing non-zero pixel values. Non-moving or background objects are subtracted out of the result image, leaving only the foreground objects formed by connected regions of non-zero pixels. 
     Simple background subtraction can separate foreground pixels from background pixels; however, changing light levels and other changes in appearance of the background can require that a new background image be acquired. Knowing when to acquire a new background image can be difficult if the scene includes moving vehicles, for example. Other techniques for foreground/background can rely on thresholds or other empirically determined parameters that can require adjustment to track changing conditions. Techniques discussed herein calculate an eccentricity ε of the pixels of a stream of video images and thereby determine foreground and background pixels in a result image derived from the stream of video images without requiring adjustment to track changing conditions. A stream of video images can be defined as a plurality of video images acquired by a video camera at successive time intervals. 
     Calculation of eccentricity ε based on an input stream of video images can be performed more efficiently by a computing device than other techniques discussed above to determine image foreground/background. For example, calculation of eccentricity ε based on an input stream of video images can be performed at a rate of hundreds of video images per second on readily available computing devices. Calculation of eccentricity ε is free of complex user-defined parameters and free of prior assumptions about the data and its distribution. 
     Eccentricity ε is a metric, i.e., value determined as explained below, that indicates how different a pixel is from past samples of the same pixel location. Regarding a set of samples of the same pixel location as a vector of variables in n-dimensions, the value of eccentricity ε increases as these variables deviate from their “normal” behavior. For foreground detection, all “abnormal” or “anomalous” pixels are labeled as foreground, based on the intensities of the pixels. The eccentricity ε at time instant k can be given by the equation: 
                     ɛ   k     =     α   +           α   ⁡     (       x   k     -     μ   k       )       T     ⁢     (       x   k     -     μ   k       )         σ   k   2                 (   1   )               
where α is an empirically determined dimensionless constant (usually a small value, e.g.  0 . 005 ) that represents a learning rate for the background model, wherein learning rate indicates what portion of the eccentricity ε k  is based on the current pixel x k , and therefore how quickly the eccentricity ε k  can adapt to changes in the input video data stream, for example, and where current pixel x k  is a vector that includes the intensities of a video data stream sample at a time k. Variables μ k  and σ k   2  are the mean and variance of the current pixel x k  at time instant k, recursively updated according to equations:
 
                     μ   k     =         (     1   -   α     )     ⁢     μ     k   -   1         +     α   ⁢           ⁢     x   k                 (   2   )                 σ   k   2     =         (     1   -   α     )     ⁢     σ     k   -   1     2       +           α   ⁡     (       x   k     -     μ   k       )       T     ⁢     (       x   k     -     μ   k       )         1   -   α                 (   3   )               
A pixel x k  is determined to be a foreground pixel when the calculated eccentricity ε k  at time instant k is higher than 5α.
 
       FIG. 4  is an example eccentricity ε k  image  400  that includes foreground regions  406 ,  408 ,  410 ,  412  on a background  402  of pixels with value zero. Foreground regions  406 ,  408 ,  410 ,  412  are determined based on applying equations (1), (2), and (3) to a stream of video images that includes video image  300 , for example. Foreground pixels in eccentricity ε k  image  400  determined by equations (1), (2), and (3) can be grouped into foreground regions  406 ,  408 ,  410 ,  412  by determining connected regions of foreground pixels, where foreground pixels are determined to be connected when they are 8-way adjacent, which includes diagonally adjacent. Foreground regions  406 ,  408 ,  410 ,  412  can represent objects that are moving against a background, for example vehicles or pedestrians on or near a roadway. 
     Traffic infrastructure systems  100  can include a plurality of stationary video cameras providing eccentricity ε k  images  400 , e.g., to a plurality of computing devices via the network  130 . For example, computers  115  in a respective plurality of vehicles  110  can receive eccentricity ε k  images  400 . Software programs included in the plurality of computing devices  115  can identify, based on information regarding the location, pose and field of view of each stationary video camera  206  and the location, speed and direction of travel of each vehicle  110 , one or more specific eccentricity ε k  images  400  that are relevant to the respective vehicles  110 . A computing device  115  in each vehicle  110  can then download, via the network  130 , only those eccentricity ε k  images  400  determined to be relevant to the respective vehicle  110 , thereby minimizing network bandwidth consumption. Each stationary video camera  206  can include information along with the eccentricity ε k  image  400  that identifies the location, pose, and field of view of the stationary video camera that acquired the eccentricity ε k  image  400 . 
       FIG. 5  is an example cognitive map  500  determined by computing device  115  in vehicle  110  based on an eccentricity ε k  image  400  and 3D location data downloaded from a traffic infrastructure system. As discussed above in relation to  FIG. 3 , 3D location data can be used to determine the real world 3D locations of objects in result image  400  based on the pixel coordinates (i, j) of the objects. A cognitive map is a representation of a local spatial environment that specifies locations of objects in the environment relative to one another. 
     In the example of  FIG. 5 , cognitive map  500  is a top-down view of a local spatial environment that includes regions and objects relevant to vehicle  110  navigation including a roadway  502  and vehicles  504 ,  506 ,  508 ,  510 . A cognitive map  500  can be determined based on information regarding the location and direction of travel of a vehicle  110  and stored map data. For example, vehicle  110  can be located at, and traveling in the direction indicated by, arrow  512 . Because cognitive map  500  is constructed based on real world 3D coordinates, real world 3D locations of objects from eccentricity ε k  image  400  can be located in cognitive map  500 . Computing device  115  can input vehicle  110  location and direction of travel and determine features of cognitive map  500  including roadway  502  based on downloaded or stored map information and data from vehicle sensors  116 . For example, a lidar sensor  116  can measure distances that confirm the presence of roadway  502 . 
     Computing device  115  can include information regarding foreground regions  406 ,  408 ,  410 ,  412  from an eccentricity ε k  image  400  downloaded from a traffic infrastructure system in a cognitive map  500 . Because the stationary video camera has included information regarding the 3D location, pose and field of view along with the eccentricity ε k  image  400 , computing device  115  can project foreground regions  406 ,  408 ,  410 ,  412  onto cognitive map  500  as moving objects  504 ,  506 ,  508 ,  510  by determining where the pixels of the foreground regions  406 ,  408 ,  410 ,  412  would intersect roadway  502  based on the pixel coordinates (i, j) of the foreground regions  406 ,  408 ,  410 ,  412  and the real world 3D location of background pixel locations including roadway  502 . By projecting foreground regions  406 ,  408 ,  410 ,  412  onto appropriate locations in cognitive map  500 , moving objects  504 ,  506 ,  508   510  can be identified by computing device  115  based on location, size and shape, and used by computing device  115  to determine a path upon which to operate vehicle  110  on roadway  502  that avoids moving objects  504 ,  506 ,  508 ,  510 . By tracking moving objects  504 ,  506 ,  508 ,  510  in a series of result images  500  acquired at successive time instants, a speed and direction can be determined for each moving object  504 ,  506 ,  508 ,  510  can be determined and used by computing device  115  to determine a path for vehicle  110  to travel on roadway  502  that avoids a collision or near-collision with moving objects  504 ,  506 ,  508 ,  510 . 
     Computing device  115  can operate vehicle  110  based on a path polynomial specifying a path  514  (dashed line) determined, at least in part, on moving objects  504 ,  506 ,  508 ,  510 . A path polynomial is a mathematical representation of real world 3D location and motion including rates of change of lateral and longitudinal accelerations, for example. Computing device  115  can determine a path polynomial  115  based on a current location, speed and direction for vehicle  110 , represented by arrow  512 . Computing device can then determine a polynomial function of degree three or less in segments called splines, wherein the segments are constrained to fit smoothly together by constraints on first derivatives to represent predicted successive locations of vehicle  110 . Constraints on path polynomial  514  in real world 3D coordinates include upper and lower limits on lateral and longitudinal accelerations and upper limits on rates of change of lateral and longitudinal accelerations (jerk) required to operate vehicle  110  along path polynomial  514 . Path polynomial  514  can also be constrained to stay in roadway  502  and to avoid moving objects  504 ,  506 ,  508 ,  510  while maintaining target speeds. Computing device  115  can operate vehicle  110  to travel along a path  514  according to a determined path polynomial by sending commands to controllers  112 ,  113 ,  114  to control steering, brakes and powertrain of vehicle  110  to cause vehicle  110  to travel along the path  514  on a roadway  502  avoiding moving objects  504 ,  506 ,  508 ,  510  at a target speed. 
     Computing device  115  can determine a path polynomial for a path  514  based on stored map data, location data from vehicle sensors  116  including GPS and accelerometers, radar, lidar and video sensors. Computing device  115  can process data from radar, lidar and video sensors to determine objects in fields of view based on each of the radar, lidar and video sensors using machine vision techniques including neural networks and Bayesian statistics, for example. Using information based on the fields of view of each sensor  116 , the objects can be located in a cognitive map  500 . Computing device can then determine a path polynomial in cognitive map  500  that permits vehicle  110  to travel to a destination while avoiding collision or near-collision with the objects by estimating free space regions and non-free space regions included in cognitive map  500 . Free space regions are regions of a cognitive map  500  in which a vehicle  110  can be predicted to travel unimpeded on a roadway surface. 
     Computing device  115  can determine destinations in cognitive map  500  for vehicle  110  travel that will be a step in accomplishing a higher level goal-directed activity like picking up a passenger and dropping them at a destination, for example. Non-free space regions included in a cognitive map  500  can include non-roadway regions and regions surrounding objects, both fixed objects like traffic cones and barriers, and, when objects are determined to be moving, locations to which the objects are likely to move, for example predicting travel for vehicles, pedestrians and cyclists. Locations in a cognitive map  500  to which the objects are likely to move can be determined based on repeated observations of the objects over time, for example, to determine object location probabilities based on determined object speed and direction. Path polynomials can be determined to direct vehicle  110  to travel within a free space region to reach a destination while avoiding non-free space regions. Data, and therefore detected objects, from vehicle sensors  116  including radar, lidar and video sensors are limited to the fields of view of each of the radar, lidar and video sensors. Adding data regarding moving objects  504 ,  506 ,  508 ,  510  to a cognitive map  500  can improve the cognitive map  500  by including objects in addition to objects visible in the fields of view of vehicle sensors  116 . 
       FIG. 6  is a diagram of a flowchart, described in relation to  FIGS. 1-5 , of a process  600  for operating a vehicle based on determining moving objects in an image. Process  600  can be implemented by a processor of computing device  115 , taking as input information from sensors  116 , and executing commands and sending control signals via controllers  112 ,  113 ,  114 , for example. Process  600  includes multiple blocks taken in the disclosed order. Process  600  also includes implementations including fewer blocks or can include the blocks taken in different orders. 
     Process  600  begins at block  602 , in which a computing device  115  included in a vehicle  110  receives an eccentricity ε k  image  400  from a stationary video camera  206 . As discussed in relation to  FIGS. 2-4  above, stationary video camera  206  can be included in a traffic infrastructure system that determines when to transmit eccentricity ε k  image  400  from stationary video camera  206  to vehicle  110 . A traffic infrastructure system can also transmit eccentricity ε k  image  400  in response to a query transmitted by vehicle  110  to a traffic infrastructure system via an ad hoc network, for example. In addition to eccentricity ε k  image  400 , a traffic infrastructure system can transmit location, pose and field of view information regarding the stationary video camera  206 . 
     At block  604  computing device  115  can combine eccentricity ε k  image  400  with a cognitive map  500  based on combining the location, pose and field of view with information regarding the real world 3D locations of objects in the field of view to determine moving objects  504 ,  506 ,  508 ,  510  in or near a roadway  502  that a vehicle  110  is traveling on as discussed above in relation to  FIGS. 4 and 5 . 
     At block  606  computing device  115  can operate vehicle  110  by determining a path polynomial  514  based on determining free space regions and non-free space regions in a cognitive map  500  and, at least in part, on moving objects  504 ,  506 ,  508 ,  510  discussed above in relation to  FIG. 5 , above. Computing device  115  can operate vehicle  110  to travel along the path polynomial  514  by sending commands to controllers  112 ,  113 ,  114  to control vehicle  110  steering, braking and powertrain. Following this block process  600  ends. 
       FIG. 7  is a diagram of a flowchart, described in relation to  FIG. 3 , of a process  700  for operating a vehicle based on determining moving objects in an image. Process  700  can be implemented by a processor of computing device  115 , taking as input information from sensors  116 , and executing commands and sending control signals via controllers  112 ,  113 ,  114 , for example. Process  700  includes multiple blocks taken in the disclosed order. Process  700  also includes implementations including fewer blocks or can include the blocks taken in different orders. 
     Process  700  begins at block  702 , where a computing device  115  included in a vehicle  110  determines a sample pixel x k  from an input image(i, j) from a stationary video camera. Process  700  describes a process applied to each pixel x k  of an input image(i, j) from a stationary video camera. The stationary video camera can be an RGB color video camera, for example, wherein the pixels are RGB pixels. The pixels x k  can be selected in raster scan order for example, where rows of the input image are scanned from top to bottom in order. Process  700  is repeated for each pixel of input image(i, j). 
     At block  704  computing device  115  updates mean μ k  according to equation (2), above, based on pixel x k  of image(i, j). 
     At block  706  computing device  115  updates the variance σ k   2  according to equation (3), above, based on pixel x k  of image(i, j) and the mean μ k . 
     At block  708  computing device  115  calculates an eccentricity image ε k  according to equation (1), above, based on pixel x k  of image(i, j), the mean μ k  and the variance σ k   2 . 
     At block  710  computing device  115  compares the pixels of eccentricity image ε k  with five times an empirically determined constant α. If the value of a pixel of eccentricity image ε k  is greater than or equal to 5α process  700  branches to block  612 . If the value of the pixel is less than 5α process  700  branches to block  614 . 
     At block  712  eccentricity image ε k  is calculated by setting output(i, j) equal to foreground. Following block  712  process  700  ends. 
     At block  714  eccentricity image ε k  is calculated by setting output(i, j) equal to background. Following block  714  process  700  ends. 
     Computing devices such as those discussed herein generally each include commands executable by one or more computing devices such as those identified above, and for carrying out blocks or steps of processes described above. For example, process blocks discussed above may be embodied as computer-executable commands. 
     Computer-executable commands may be compiled or interpreted from computer programs created using a variety of programming languages and/or technologies, including, without limitation, and either alone or in combination, Java™, C, C++, Visual Basic, Java Script, Perl, HTML, etc. In general, a processor (e.g., a microprocessor) receives commands, e.g., from a memory, a computer-readable medium, etc., and executes these commands, thereby performing one or more processes, including one or more of the processes described herein. Such commands and other data may be stored in files and transmitted using a variety of computer-readable media. A file in a computing device is generally a collection of data stored on a computer readable medium, such as a storage medium, a random access memory, etc. 
     A computer-readable medium includes any medium that participates in providing data (e.g., commands), which may be read by a computer. Such a medium may take many forms, including, but not limited to, non-volatile media, volatile media, etc. Non-volatile media include, for example, optical or magnetic disks and other persistent memory. Volatile media include dynamic random access memory (DRAM), which typically constitutes a main memory. 
     Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, an EPROM, a FLASH-EEPROM, any other memory chip or cartridge, or any other medium from which a computer can read. 
     All terms used in the claims are intended to be given their plain and ordinary meanings as understood by those skilled in the art unless an explicit indication to the contrary in made herein. In particular, use of the singular articles such as “a,” “the,” “said,” etc. should be read to recite one or more of the indicated elements unless a claim recites an explicit limitation to the contrary. 
     The term “exemplary” is used herein in the sense of signifying an example, e.g., a reference to an “exemplary widget” should be read as simply referring to an example of a widget. 
     The adverb “approximately” modifying a value or result means that a shape, structure, measurement, value, determination, calculation, etc. may deviate from an exactly described geometry, distance, measurement, value, determination, calculation, etc., because of imperfections in materials, machining, manufacturing, sensor measurements, computations, processing time, communications time, etc. 
     In the drawings, the same reference numbers indicate the same elements. Further, some or all of these elements could be changed. With regard to the media, processes, systems, methods, etc. described herein, it should be understood that, although the steps or blocks of such processes, etc. have been described as occurring according to a certain ordered sequence, such processes could be practiced with the described steps performed in an order other than the order described herein. It further should be understood that certain steps could be performed simultaneously, that other steps could be added, or that certain steps described herein could be omitted. In other words, the descriptions of processes herein are provided for the purpose of illustrating certain embodiments, and should in no way be construed so as to limit the claimed invention.