An efficient framework for visual target tracking detection and action recognition

Abstract

This research presents a novel comprehensive framework for visual target tracking, detection, and action recognition to overcome some existing issues such as feature misalignment, occlusion handling, scale variations, complex backgrounds, and network issues. The first contribution of the research work is the development of an effective technique for detecting multi-scale and arbitrarily oriented objects. A revised single-stage detector is proposed for localizing and detecting oriented and multi-scale objects. The proposed detector uses an enhanced feature refinement module that reconstructs an enhanced feature map for accurate localization and detection of oriented and multi-scale targets. The second contribution is the proposal of an efficient algorithm for detecting and tracking visual targets in dense environments. The novel tracking system is modelled based on a correlation filtering-based approach. The proposed tracker partitions a target based on its size and shape to learn the discriminative features in each partition. Based on the correlation output (APCE value), the occlusion status is mapped for each partition. The proposed tracker includes a redetection mechanism to improve the efficiency of the tracker and to reduce its computation complexity. newline