Abstract
Multiple object tracking (MOT) is one of the most important research areas in visual surveillance. However, some practical challenges remain to be overcome for implementing this technology, such as occlusion, missed detection, false detection, and abrupt camera motion. In this paper, to the visual multi-object tracking, a novel fuzzy data association algorithm is proposed. In order to incorporate expert experience into the proposed algorithm, a fuzzy inference system based on knowledge is designed, and the fuzzy membership degrees are used to substitute the association probabilities between the objects and observations. The experiment results on several public data sets show that the proposed algorithm has advantages over other state-of-the-art tracking algorithms in terms of efficiency.
Highlights
The objective of multi-object tracking is to estimate the current states of objects based on previous visual measurements up to the current time in a video sequence, such as positions, size, identification (ID), etc
In the fuzzy inference system, five fuzzy sets that are labeled in the linguistic terms of zero (ZE), small positive (SP), medium positive (MP),large positive (LP), and very large positive (VP), are specified for each crisp input ( Ei, j k and 'Ei, j k )
In order to compare the performance of the proposed algorithm with other multiple tracking algorithms, we chose two reported state-of-art trackers, such as Bae et al’s proposed method[7] and OM+APP[4]
Summary
Abstract—Multiple object tracking (MOT) is one of the most important research areas in visual surveillance. Some practical challenges remain to be overcome for implementing this technology, such as occlusion, missed detection, false detection, and abrupt camera motion. To the visual multi-object tracking, a novel fuzzy data association algorithm is proposed. In order to incorporate expert experience into the proposed algorithm, a fuzzy inference system based on knowledge is designed, and the fuzzy membership degrees are used to substitute the association probabilities between the objects and observations. The experiment results on several public data sets show that the proposed algorithm has advantages over other state-of-the-art tracking algorithms in terms of efficiency
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