Real-time tracking of multiple objects with locally adaptive correlation filters

Abstract

A tracking algorithm using locally adaptive correlation filtering is proposed. The algorithm is designed to track multiple objects with invariance to pose, occlusion, clutter, and illumination variations. The algorithm employs a prediction scheme and composite correlation filters. The filters are synthesized with the help of an iterative algorithm, which optimizes discrimination capability for each target. The filters are adapted online to targets changes using information of current and past scene frames. Results obtained with the proposed algorithm using real-life scenes, are presented and compared with those obtained with state-of-the-art tracking methods in terms of detection efficiency, tracking accuracy, and speed of processing.

Multiple Object Tracking in Deep Learning Approaches: A Survey

“…IoU tracker to make possible 100K fps. Ruchay et al proposed a locally adaptive correlation filter with CNN [65], which adapted scene frame information into filters. Sharma et al's model is for urban driving using a single camera, which complements the error in detection using pairwise costs using 3D cues [62] and performs in real-time.…”