[Show abstract][Hide abstract] ABSTRACT: We propose a method for enhancing the stability of tracking people by incorporating long-term observations of human actions in a scene. Basic human actions, such as walking or standing still, are frequently observed at particular locations in an observation scene. By observing human actions for a long period of time, we can identify regions that are more likely to be occupied by a person. These regions have a high probability of a person existing compared with others. The key idea of our approach is to incorporate this probability as a bias in generating samples under the framework of a particle filter for tracking people. We call this bias the environmental existence map (EEM). The EEM is iteratively updated at every frame by using the tracking results from our tracker, which leads to more stable tracking of people. Our experimental results demonstrate the effectiveness of our method.
Motion and video Computing, 2008. WMVC 2008. IEEE Workshop on; 02/2008
[Show abstract][Hide abstract] ABSTRACT: In this paper, we propose a novel method to learn motion patterns and detect anomalies by human trajectory analysis. Human trajectories are various, for example, moving, roaming, pausing, and so on. But, current approaches for the analysis of motion patterns are effective only in understanding simple trajectories. We aim to understand complicated human trajectories with long-term observation. To deal with spatial and temporal features of trajectories, we employ HMM (Hidden Markov Model) to model time-series features of human positions. Next, a similarity matrix of HMM mutual distances is formed. MDS (Multi-Dimensional Scaling) based on eigenvector decomposition provides projected coordinates of trajectories in low-dimensional space. Then we apply k-means clustering to projected data in order to acquire human motion patterns. Anomalies can be detected by the use of likelihood scores for HMM representing motion patterns. We tested the proposed method by real-world trajectories data observed in a small store. Experimental result shows that our method accurately finds typical motion patterns and unusual trajectories.
Systems, Man and Cybernetics, 2007. ISIC. IEEE International Conference on; 11/2007
[Show abstract][Hide abstract] ABSTRACT: This paper presents a high-efficiency low-reradiation planar rectifying antenna (rectenna). The rectenna is composed of a high-gain cavity-backed circular patch antenna using novel coplanar feeding structure and a low-profile microstrip rectifying circuit. The cavity-backed antenna achieved the gain of 9.17dBi and the -20dB return loss bandwidth of approximately 1.4%. The rectifying circuit has a harmonics filter that suppresses the reradiated harmonics under -59 dBc. The RF-to-dc conversion efficiency of the rectenna element was measured as 71.4% with a 266mW input power. Over 13W DC output power was demonstrated using the 52- element triangular array.