Abstract
Abstract This article presents a robust approach to tracking multiple vehicles with integration of multiple visual features. The observation is modeled by democratic integration strategies according to the reliability of the information in the current multi-visual features to adjust their weights. The appearance model is also embedded in a particle filter (PF) tracking framework. Furthermore, we propose a new model updating algorithm based on the PF. In order to avoid incorrect results caused by "model drift" introduced into the observation model, model updating should only be controlled in a reliable manner, and the rate of updating is based on reliability. This article also presents the experiments using a real video sequence to verify the proposed method.
Highlights
With the rapid process of urbanization, the concept of developing a “smart city” has gained prominence
A color histogram and an edge orientation histogram (EOH) are selected as visual features to model the observation of the vehicle and integrated by a democratic integration strategy proposed by Triesch and Malsburg [21]
In order to avoid errors caused by model drift, the updating process should only be implemented in a reliable manner, and the rate of updating can be controlled according to this reliability
Summary
With the rapid process of urbanization, the concept of developing a “smart city” has gained prominence. The proposed model has strong robustness to changes in both illumination and shape It fails to track when an object is occluded by one with the same visual features for even a moment. This article proposes a robust tracking approach with an adaptive integration of multiple visual features for vehicles. A color histogram and an edge orientation histogram (EOH) are selected as visual features to model the observation of the vehicle and integrated by a democratic integration strategy proposed by Triesch and Malsburg [21] It is suitable for dynamic scenes due to the adaptive adjustment of the weight of each visual feature with its reliability in the current frame.
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