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In this paper, a running person detection method is proposed for the community patrol robot. The challenges include the diversity of movement direction and the ego-motion of camera. The diversity of movement direction means that it is difficult to gain high accuracy detection by only using appearance information. The ego-motion of camera means that the motion information contains high noise. To address...
We propose a novel approach for the crowd anomaly detection in multiple cameras with non-overlapping and visible views. As we all know that there are some kinds of information hidden in the non-overlapping fields always. In this paper, we will mine time dependence data so that we can analyze the crowd anomaly detection from time dimension's angle. Firstly, we have to preprocess the real scene using...
In this paper, we present a real-time running detection system from a moving camera. 11 fps and satisfying detection accuracy in outdoor surveillance environment can be achieved in the system, using only one processing thread without resorting to special hardware like GPU. Real-time and high accuracy detection are made possible by two contributions. First, we use a succession of preprocessing methods...
This paper presents a method to analyze crowd with computer vision techniques in virtual environments. To overcome the difficulty of obtaining video evidence in hazard situations, or, to meet the demand of big data for machine learning methods, we attempt to use virtual models to simulate actual ones. To prove the reliability of virtual crowd models we simulated in three situations where people walk...
Most existing methods for abnormal event detection in the literature are relied on a training phase. Different from conventional approaches for abnormal event detection, a saliency attention based abnormal event detection approach is proposed in this paper. It is inspired by the visual attention mechanism that abnormal events are those which attract attention mostly in videos. The temporal and spatial...
In this paper, we present an automatic method to remove shadows in light field images. Taking into account the internal structure of the light field data, depth map of the captured scene is extracted to calculate the surface normal. Using nonlocal matching by combining chromaticity, normal and spatial location information in an anisotropic window, the shadow confidence of each pixel is established...
We propose a novel framework for fast and robust video anomaly detection and localization in complicated crowd scenes. Images of each video are split into cells for extracting local motion features represented by optical flow. In the train videos, most background cells are subtracted by ViBe model. Feature vectors are extracted from each cell by integrating the value of optical flow in 8 different...
In this paper we present a method to detect and localize abnormal events in crowded scene. Most existing methods use the patch of optical flow or human tracking based trajectory as representation for crowd motion, which inevitably suffer from noises. Instead, we propose the employment of a new and efficient feature, short-term trajectory, which represent the motion of the visible and constant part...
In this paper, we propose a new approach to detect hand-waving motion in crowds. Different from previous approaches which are often based on segmentation and motion detection, our method can be seen as a complexity reduction process from the problem of 3D motion detection to 2D object detection. Through arranging the same row of per video frame along the time sequence, we obtain 2D images composed...
Abnormal behavior detection has recently gained growing interest from computer vision researchers. In this paper, the gait-analysis-based abnormal detection is proposed for walking scenes, where gaits of people are analyzed in all kinds of situations and the gait data are utilized to construct the basic gait model. Walking people in the crowd are tracked and their activities silhouettes are abstracted...
Crowd control and management is a very important task in public places. Historically, many crowd disasters happened because of the loss of control of the crowd flow direction. This paper presents an intelligent surveillance system based on RANSAC (Random Sample Consensus) algorithm, which can estimate the crowd flow direction and classify people into different crowd groups. We calculate the optical...
During the past few years, much effort has been made to enhance the performance of visual tracking by combining multiple features. In this paper, we propose a tracking algorithm based on region correlation descriptor, which can provide an elegant solution to fuse multiple features for representing the object Besides, we suggest a method to find the best matching region by calculating a centroid estimated...
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