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Person re-identification is known as matching an individual captured in one or more cameras using a gallery of provided candidates from a different camera view. It is a hard task owing to variations in illumination, viewpoints, poses and small number of annotated training individuals. For obtaining the proper distance metrics, we propose a novel approach based on dictionary learning. Our method decomposes...
In recent years, designing and testing video anomaly detection methods have focused on synthetic or unrealistic sequences. This has mainly four drawbacks: 1) events are controlled and predictable because they are usually performed by actors; 2) environmental conditions, e.g. camera motion and illumination, are usually ideal thus realistic conditions are not well reflected; 3) events are usually short...
Associating groups of people across non-overlapping camera views is an important but unsolved problem. Compared with the similar person re-identification task, group re-identification introduces some new challenges, such as significant deformation in uncontrolled directions, great intra-group occlusions and so on. In this paper, we propose a novel patch matching based framework for group re-identification...
We introduce a new mono-camera system with multi-infrared lights for human posture recognition, which is based on the 3D body shape recovered from the body silhouette and cast shadows. We propose a new voxelization method inspired from the Shape From Silhouettes (SFS) approach for Visual Hull (VH) reconstruction. Our setup consists of 4 infrared lights installed in the different upper corners of a...
This paper presents a novel and unsupervised approach for discovering “sudden” movements in video surveillance videos. The proposed approach automatically detects quick motions in a video, corresponding to any action. A set of possible actions is not required and the proposed method successfully detects potentially alarm-raising actions without training or camera calibration. Moreover, the system...
In this work we propose an architecture for fully automated person re-identification in camera networks. Most works on re-identification operate with manually cropped images both for the gallery (training) and the probe (test) set. However, in a fully automated system, re-identification algorithms must work in series with person detection algorithms, whose output may contain false positives, detections...
This paper proposes a video anomaly detection method based on wake motion descriptors. The method analyses the motion characteristics of the video data, on a video volumeby- video volume basis, by computing the wake left behind by moving objects in the scene. It then probabilistically identifies those never previously seen motion patterns in order to detect anomalies. The method also considers the...
In this paper we propose a classification-based automated surveillance system for multiple-instance object retrieval task, and its main purpose, to track of a list of persons in several video sources, using only few training frames. We discuss the perspective of designing appropriate motion detectors, feature extraction and classification techniques that would enable to attain high categorization...
Video surveillance is used to monitor activities of people and to take necessary actions when an abnormal activity is noticed. The initial step of video surveillance is object detection, which is done by background subtraction. In this paper, we present a multiple camera-based object detection method under camouflage i.e., when the background color is almost similar to the foreground color. In our...
We address the problem of learning robust and efficient multi-view object detectors for surveillance video indexing and retrieval. Our philosophy is that effective solutions for this problem can be obtained by learning detectors from huge amounts of training data. Along this research direction, we propose a novel approach that consists of strategically partitioning the training set and learning a...
In this paper, a selective eigenbackgrounds method is proposed for background subtraction in crowded scenes. In order to train and update the eigenbackground model with frames containing few objects (i.e. clean frames), virtual frames are constructed based on a frame selection map. Then, the eigenbackground that best depicts background is selected for each pixel based on an eigenbackground selection...
We present a novel approach for vehicle detection in urban surveillance videos, capable of handling unstructured and crowded environments with large occlusions, different vehicle shapes, and environmental conditions such as lighting changes, rain, shadows, and reflections. This is achieved with virtually no manual labeling efforts. The system runs quite efficiently at an average of 66Hz on a conventional...
This paper presents a novel method to count people for video surveillance applications. Methods in the literature either follow a direct approach, by first detecting people and then counting them, or an indirect approach, by establishing a relation between some easily detectable scene features and the estimated number of people. The indirect approach is considerably more robust, but it is not easy...
For target tracking across multiple cameras with disjoint views, previous works usually employed multiple cues and focused on learning a better matching model of each cue, separately. However, none of them had discussed how to integrate these cues to improve performance, to our best knowledge. In this paper, we look into the multi-cue integration problem and propose an unsupervised learning method...
In this paper, we propose a strategy of multi-SVM incremental learning system based on Learn++ classifier for detection of predefined events in the video. This strategy is offline and fast in the sense that any new class of event can be learned by the system from very few examples. The extraction and synthesis of suitably video events are used for this purpose. The results showed that the performance...
This paper presents a novel method to count people for video surveillance applications. The problem is faced by establishing a mapping between some scene features and the number of people. Moreover, the proposed technique takes specifically into account problems due to perspective. In the experimental evaluation, the method has been compared with respect to the algorithm by Albiol et al., which provided...
Early warning systems are critical in providing emergency response in the event of unexpected hazards. Cheap cameras and improvements in memory and computing power have enabled the design of fire detectors using video surveillance systems. This is critical in scenarios where traditional smoke detectors cannot be installed. In such scenarios, it has been observed that the smoke is visible well before...
Multi-view tracking of objects in video surveillance consists in segmenting and automatically following them through different camera views. This may be achieved using geometric methods, e.g. by calibrating camera sensors and using their transformation matrices. However, in practice the precision of calibration is a major issue when trying to achieve this task robustly. In this paper, we present an...
In this paper we present robust statistical methods for segmentation and preprocessing of time-multiplexed videos. We will present hidden Markov and hidden semi-Markov model based real-time detectors and their usage for video segmentation and anomalous event detection. We demonstrate the high performance of our detectors with real-life outdoor videos from low-quality cameras.
The most prevailing approach now for parking lot vacancy detecting system is to use sensor-based techniques. The main impediments to the camera-based system in applying to parking lots on rooftop and outside building are the glaring sun light and dark shadows in the daytime, and low-light intensity and back-lighting in the nighttime. To date, no camera-based detecting systems for outdoor parking lots...
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