Multiple object localization and tracking based on boosted efficient binary local image descriptor

Tracking multiple objects being an important problem in video surveillance. In the research, we provide a BEBLID (Boosted Efficient Binary Local Image Descriptor feature)- based tracking system. BEBLID being quick binary descriptor that works similarly to ORB, SIFT, or SURF and requires little processing. BEBLID key points and their related descriptions for the objects are first generated from two neighboring frames. The best match is then found by computing the Hamming distance between these two-point sets. The following localization of the objects may then be deduced using the key points that match. At the same, the object detection being facilitated by YOLOv3. Combined efforts from the two i.e., BEBLID and YOLOv3 being utilized for precise localization of the multiple objects. Identification of the localization of objects over time leads to the tracking mechanism. The effectiveness of our tracking technology is evaluated using datasets representing actual surveillance scenarios. The outcomes of the experiments demonstrate the suggested approach being capable of accurately and successfully tracking objects. • We proposed a multiple object tracking algorithm based on Boosted Efficient Binary Local Image Descriptor feature. • This algorithm utilizes the competencies of BEBLID and YOLOv3 to effectively detect and track multiple objects. • We validated this algorithm by comparing the results with other state-of-art algorithms presented in the literature.