In order to avoid the deficiencies of conventional high voltage circuit breaker mechanical properties detection methods, a new algorithm based on image block matching with diamond search strategy is presented in this paper. The motion of auxiliary mark on the pull rod or shaft is firstly recorded by a high-speed and high-definition digital camera when the circuit breaker is open or close. Then the motion trajectory is acquired through diamond image block matching method. The mechanical parameters, such as travel and open and close velocity, are calculated according to the travel-time curve of the circuit breaker. Finally, evaluation model is constructed taking mechanical parameters characteristic values as inputs of ELM. Comparing to the existing techniques, our method is a noncontact measurement based on computer vision. It is easy and convenient for practical application since it need not any electrical and mechanical connection to the breaker. Another advantage of our method is that it can obtain the line and angle displacement simultaneously. The experiment results on the circuit breaker of 220 kv show that our method is effective for breaker mechanical properties detection.
Abstract. This paper proposes a new method of semi-supervised human action recognition. In our approach, the motion energy image(MEI) and motion history image(MHI) are firstly used as the feature representation of the human action. Then, the constrained semi-supervised kmeans clustering algorithm is utilized to predict the class label of unlabeled training example. Meanwhile the average motion energy and history images are calculated as the recognition model for each category action. The category of the observed action is determined according to the correlation coefficients between its feature images and the pre-established average templates. The experiments on Weizmann dataset demonstrate that our method is effective and the average recognition accuracy can reach above 90% even when only using very small number of labeled action sequences.
A modified particle swarm optimization (MPSO) algorithm is presented. In the new algorithm, particles not only studies from itself and the best one but also from other individuals. By this enhanced study behavior, the opportunity to find the global optimum is increased and the influence of the initial position of the particles is decreased At last, the method adopting MPSO algorithm to solve the optimal power flow problem is given. The numeric simulation for a 5-bus system shows that this algorithm is feasible to solve optimal power flow problem.
Based on the observation that motion of different pixels from the same target has very similar spatial-temporal properties in bus video surveillance images, a feature point's trajectory clustering method is proposed to estimate passenger flow in this paper. Firstly, the pyramid-based optical flow algorithm is utilized to tracking the feature point's movement in the images; then, their trajectories are pre-classified into passenger getting on, off the bus and others according their motion direction histogram; finally, the pre-classified trajectories are clustered by their spatial-temporal similarity and the cluster number is looked as the result of bus passenger flow estimation. Since it needn't to detect the head contour, face or other features of the passenger, our method is simple, fast and strong. The experiment results on multiple real bus surveillance videos show that it has high counting accuracy (>90%) in different illumination, background and even crowded conditions.
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