Open AccessObject Detection using Particle Swarm Optimisation and Kalman Filter to Track Partially occluded Targets
Author(s) -
Haridwar Singh,
Millie Pant,
Sudhir Khare
Publication year - 2022
Publication title -
defence science journal/defence science journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.198
H-Index - 32
eISSN - 0976-464X
pISSN - 0011-748X
DOI - 10.14429/dsj.72.17502
Subject(s) - kalman filter , computer vision , artificial intelligence , computer science , particle swarm optimization , tracking (education) , video tracking , particle filter , convergence (economics) , extended kalman filter , fast kalman filter , covariance matrix , object detection , algorithm , object (grammar) , pattern recognition (psychology) , psychology , pedagogy , economics , economic growth
Motion estimation, object detection, and tracking have been actively pursued by researchers in the field of real time video processing. In the present work, a new algorithm is proposed to automatically detect objects using revised local binary pattern (m-LBP) for object detection. The detected object was tracked and its location estimated using the Kalman filter, whose state covariance matrix was tuned using particle swarm optimisation (PSO). PSO, being a nature inspired algorithm, is a well proven optimization technique. This algorithm was applied to important real-world problems of partially-occluded objects in infrared videos. Algorithm validation was performed by realizing a thermal imager, and this novel algorithm was implemented in it to demonstrate that the proposed algorithm is more efficient and produces better results in motion estimation for partially-occluded objects. It is also shown that track convergence is 56% faster in the PSO-Kalman algorithm than tracking with Kalman-only filter.