Vehicle Tracking in Video using Fractional Feedback Kalman Filter
Harpreet Kaur, Jyotindra Singh Sahambi · IEEE Transactions on Computational Imaging · 2016
Object tracking is a challenging and important area of research. The object tracking system must be capable of tracking abrupt variations in object state. Kalman filter is fundamental and widely used as an optimal state estimator in object tracking. With known noise and system parameters, Kalman filter tends to stabilize the gain. However, during sudden transitions of the object tracked, constant gain Kalman filter may diverge. This paper proposes a modified steady-state gain of the Kalman filter, which is achieved by introducing a fractional feedback loop across the Kalman gain. The modified Kalman gain is estimated by minimizing the cost function of the proposed Kalman filter. Results show that the accuracy and robustness of the Kalman filter are significantly improved. The performance of the proposed method is compared with that of the standard Kalman filter, fractional-order Kalman filter, and unscented Kalman filter. In this work, the root mean square error is used as the performance metric. The proposed method has been tested for different datasets, including traffic videos. Experiments show that RMSE is improved upto $17\%$ by using the proposed modified Kalman filter.