A hybrid optimisation enabled deep learning for object detection and multi-object tracking

The potential of multi-object tracking (MOT) in academia and industry has drawn growing attention. Despite the various methods that have been suggested to address this issue, it continues to be difficult because of things like sudden changes in appearance and severe object occlusions. In this paper, a Jaya political search optimisation (Jaya-PSO) enabled ShuffleNet is developed for object detection (OD) and MOT. Initially, the input video is fed to video frame extraction. The extracted frames are fed into the object segmentation phase, where the segmentation is done by the mask-regional convolutional neural network (Mask-RCNN), trained by tangent squirrel search optimisation (TSSO). Here, TSSO is the integration of the tangent search algorithm (TSA) and squirrel search optimisation (SSO). Then, the object recognition is performed using ShuffleNet trained by Jaya-PSO, where the Jaya-PSO is from the Jaya algorithm, political optimiser (PO) and TSA. Finally, MOT is done by the Henry gas solubility optimised unscented Kalman filtering (HGSO-based UKF). The HGSO-based UKF is the integration of Henry gas solubility optimisation (HGSO) and unscented Kalman filtering (UKF). The measures utilised for analysis are accuracy, sensitivity, specificity and multiple object tracking precision (MOTP). The proposed method attained 92.9% accuracy, 92.1% sensitivity, 92.9% specificity, and 91.0% MOTP.