In this paper, a trajectory learning-based vehicle tracking algorithm is presented which is a new top-down vehicle tracker. The history of trajectory is learnt by a novel sptio-temporal data base known center transition matrix, CTM. At first, the CTM is constructed on c More
In this paper, a trajectory learning-based vehicle tracking algorithm is presented which is a new top-down vehicle tracker. The history of trajectory is learnt by a novel sptio-temporal data base known center transition matrix, CTM. At first, the CTM is constructed on centers which are obtained using fuzzy clustering on vehicle trajectories. The i, j-th element of CTM indicates passing of the object from center i to center j in two consecutive frames which CTM is completed by multi-object tracking. The CTM is efficient in search of similar blobs in image sequences and can determine the radius and region of search and increasing of convergence rate of RLS predictor. The proposed tracking algorithm is tested in the intersection of a highway to a square which gives good results.
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Existence of a high inter-channel correlation in a stereo communication system results in a considerable performance degradation in the associated stereo acoustic echo canceller and also weight misalignment of adaptive filters even after finalizing the convergence perio More
Existence of a high inter-channel correlation in a stereo communication system results in a considerable performance degradation in the associated stereo acoustic echo canceller and also weight misalignment of adaptive filters even after finalizing the convergence period. In this paper an approach for improving the performance of NLMS adaptive filter is developed based on reducing the correlation of input signals employing a multi-input-multi-output decorrelation network. This approach has a low-complexity neural network structure and can train in a real-time manner. Simulation results show an improvement in weight convergence rate and misalignment employing the proposed method.
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