Stereo Based Tracking-by-Detection for Visual Sensor Networks

Visual binary descriptors have successfully been employed in several applications such as visual search, object recognition and visual tracking. In particular, binary descriptors are suitable for scenarios where computational, storage and energy resources are constrained and have been previously exploited to track an object along a video sequence. In this paper, binary descriptors are used to perform visual tracking in a stereo-based system, i.e. when two cameras with overlapping views are employed in a cooperative way. The proposed stereo-based visual tracker follows the tracking-by-detection approach where features extracted from different cameras are used to characterize the object appearance with a suitable model. Moreover visual tracking is performed at a central controller by just using the features transmitted from the two camera nodes. To achieve this target, efficient coding techniques are proposed to reduce the amount of feature data that is transmitted through the network. The performance of the proposed stereo-based visual tracker is evaluated in terms of rate-accuracy, i.e. using quantitative metrics to assess the accuracy of the visual tracker as a function of the coding bitrate.