Intelligent feature-guided multi-object tracking using Kalman filter

Kalman filtering, a recursive state estimation filter is a robust method for tracking objects. It has been proven that Kalman filter gives a good estimation when tested on various tracking systems. However, unsatisfying tracking results may be produced due to different real-time conditions. These conditions include: inter-object occlusion and separation which are observed when objects are being tracked in real-time. Thus, it is challenging to handle for the classical Kalman filter. In this paper, we proposed an idea of intelligent feature-guided tracking using Kalman filtering. A new method is developed named correlation-weighted histogram intersection (CWHI), in which correlation weights are applied to histogram intersection (HI) method. We focus on multi-object tracking in traffic sequences and our aim is to achieve efficient tracking of multiple moving objects under the confusing situations. The proposed algorithm achieves robust tracking with 97.3% accuracy and 0.07% covariance error in different real-time scenarios.