Fuzzy region assignment for visual tracking

Abstract

In this work we propose a new approach based on fuzzy concepts and heuristic reasoning to deal with the visual data association problem in real time, considering the particular conditions of the visual data segmented from images, and the integration of higher-level information in the tracking process such as trajectory smoothness, consistency of information, and protection against predictable interactions such as overlap/occlusion, etc. The objects' features are estimated from the segmented images using a Bayesian formulation, and the regions assigned to update the tracks are computed through a fuzzy system to integrate all the information. The algorithm is scalable, requiring linear computing resources with respect to the complexity of scenarios, and shows competitive performance with respect to other classical methods in which the number of evaluated alternatives grows exponentially with the number of objects.

Information Fusion: Popular Approaches and Applications

“…Fuzzy logic algorithms do well in pattern recognition [59], tracking [60], position, spatial road forecast [61], and decision-making [62].…”

Fuzzy human motion analysis: A review

“…Besides that, in the multiple objects tracking, recovering from the overlapping regions is a big challenge. In order to solve this, [105,106] applied FIS to update both the trajectories and the shape estimated for the targets with a set of image regions. These image regions are represented using the blobs extracted from each frame.…”

“…While in the multiple object tracking, recovering from the overlapping regions is a big challenge. [105,106] FIS is applied to perform the fuzzy shape estimation to achieve a better tracking performance by taking into account the uncertainty in shape estimation. If the shape is uncertain, the tracking will be locked and it will be recovered once the confidence becomes higher.…”