Multiple and variable target visual tracking for video-surveillance applications

Visual detection and tracking are interdisciplinary tasks which are oriented at estimating the state of one or multiple moving objects in a video sequence. This is one of the first tasks in processing video systems which try to describe human behaviour in different contexts, such as video-surveillance, sport technique analysis. This work presents a multiple object tracking system which properly hybridizes particle filters and memetic algorithms to produce a more reliable and efficient tracking algorithm. The system has been tested on synthetic and real image sequences, with the aim of describing their performance for different levels of noise, occlusions, a variable number of objects, etc. Experimental results demonstrate that the proposed system accurately tracks multiple objects in the scene, by grouping and ungrouping them when necessary, while keeping their identities during the sequence of images. Moreover, the performance of the proposed system is not strongly affected by the increase in the number of objects, maintaining computational load and precision in proper balance.