Abstract: An intelligent lighting system employs energy efficient outdoor lighting and intelligent sensor technology in cooperation with video analytics processing. The lighting system selectively illuminates outdoor spaces and identifies and evaluates events in a scene monitored by a video camera, thereby to facilitate proactive and appropriate security responses to those events. Selective use of advanced lighting fixtures may significantly reduce costs of lighting areas that are monitored by security systems such as streets, public parks, and parking lots, while simultaneously improving security, safety, and traffic control. Energy savings alone, for a properly designed system, are estimated at 50%-90% of current usage. When combined with remote monitoring, such systems may prevent accidents and criminal activity.

Abstract: Video analytics is used to track an object of interest represented in video data representing the field of view of a scene observed by a video camera. A multi-dimensional virtual beam is used to detect whether the tracked object of interest is continually present in a detection zone within the field view of the scene. An occurrence of an event is signaled when the tracked object of interest is continually present in the detection zone during a period beginning when the tracked object of interest enters the detection zone and ending when the tracked object of interest leaves the detection zone through the opposite side, after having completely crossed through the detection zone. Use of a virtual beam detection zone reduces false alarms as compared to the numbers of incidences of false alarms of traditional detection methods, while adding several features and benefits.

Abstract: A camera system comprises an image capturing device, object detection module, object tracking module, and match classifier. The object detection module receives image data and detects objects appearing in one or more of the images. The object tracking module temporally associates instances of a first object detected in a first group of the images. The first object has a first signature representing features of the first object. The match classifier matches object instances by analyzing data derived from the first signature of the first object and a second signature of a second object detected in a second image. The second signature represents features of the second object derived from the second image. The match classifier determine whether the second signature matches the first signature. A training process automatically configures the match classifier using a set of possible object features.