Object Detection using Particle Swarm Optimisation and Kalman Filter to Track Partially-occluded Targets

Abstract

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.


Flowchart depicting PSO algorithm.
… 


Flowchart of m-lbP algorithm.
… 


Thresholding of a value by comparing it to its neighbouring elements.
… 


(a) Photograph of thermal imager used and (b) reference thermal image of house.
… 


+4
(a) Template of man

Description