Descriptor-based adaptive tracking-by-detection for visual sensor networks
Local descriptors represent a powerful tool, which is exploited in several applications such as visual search, object recognition and visual tracking. Real-valued visual descriptors such as SIFT and SURF achieve state-of-the-art accuracy performance for a large set of visual analysis tasks. However, such algorithms are demanding in terms of computational capabilities and bandwidth, being unsuitable for scenarios where resources are constrained. In this context, binary descriptors provide an efficient alternative to real-valued descriptors, due to their low computational complexity, limited memory footprint and fast matching algorithms. In this paper, binary descriptors are used to perform visual tracking of an object along time. The proposed visual tracker performs descriptor matching between consecutive frames, applies filtering techniques to remove undesirable outliers and employs a suitable model to characterize the object appearance. In addition, techniques to code and transmit these description streams are employed, thus reducing the amount of data necessary to transmit to perform accurate object tracking. The efficiency of the proposed visual tracker is evaluated in terms of rate-accuracy, i.e. using the bitrate associated to the compressed binary descriptors and a quantitative metric to assess the accuracy of the visual tracker.