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Object tracking is a paramount task in video surveillance systems. Although many efforts have been accomplished on object tracking during the last years more work is still needed in order to generate more robust systems. A new fuzzy method for object tracking is presented in this paper. The proposed method is composed of two Sugeno type systems with weighted average memory output functions. One of...
This paper proposes a new road traffic monitoring method based on image processing and particle filtering. The proposed method detects and classifies automatically moving vehicles in previously defined classes. The detected vehicles are tracked using a new particle filtering algorithm to determine their positions on the road at each time, and then the vehicle positions are used to estimate its trajectory...
We proposes an unsupervised method to address video object extraction (VOE) in uncontrolled videos, i.e. videos captured by low-resolution and freely moving cameras. We advocate the use of dense optical-flow trajectories (DOTs), which are obtained by propagating the optical flow information at the pixel level. Therefore, no interest point extraction is required in our framework. To integrate color...
This paper proposes a novel method for tracking failure detection. The detection is based on the Forward-Backward error, i.e. the tracking is performed forward and backward in time and the discrepancies between these two trajectories are measured. We demonstrate that the proposed error enables reliable detection of tracking failures and selection of reliable trajectories in video sequences. We demonstrate...
This article introduces a new particle filtering approach for object tracking in video sequences. The projective particle filter uses a linear fractional transformation, which projects the trajectory of an object from the real world onto the camera plane, thus providing a better estimate of the object position. In the proposed particle filter, samples are drawn from an importance density integrating...
We present an activity recognition feature inspired by human psychophysical performance. This feature is based on the velocity history of tracked keypoints. We present a generative mixture model for video sequences using this feature, and show that it performs comparably to local spatio-temporal features on the KTH activity recognition dataset. In addition, we contribute a new activity recognition...
With the continuous improvements in computer-vision techniques, automatic low-cost video surveillance gradually emerges for consumer applications. Successful trajectory estimation and human-body modeling facilitate the semantic analysis of human activities in video sequences. We propose a fast analyzer for surveillance video, which aims at automatic analysis of human behavior and semantic events....
We present a novel approach for multi-object tracking which considers object detection and spacetime trajectory estimation as a coupled optimization problem. Our approach is formulated in a minimum description length hypothesis selection framework, which allows our system to recover from mismatches and temporarily lost tracks. Building upon a state-of-the-art object detector, it performs multiview/multicategory...
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