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Image processing is an inevitable tool for visual tracking. Visual object tracking is a very hot area of research in the computer vision. Computer vision tasks include methods for acquiring, processing, analyzing and understanding digital images, and in general, deal with the extraction of high-dimensional data from the real world in order to produce numerical or symbolic information, e.g., in the...
In this paper a novel view-invariant movement recognition method is presented. A multi-camera setup is used to capture the movement from different observation angles. Identification of the position of each camera with respect to the subject's body is achieved by a procedure based on morphological operations and the proportions of the human body. Binary body masks from frames of all cameras, consistently...
Anomaly detection in crowd scene is very important because of more concern with people safety in public place. This paper presents an approach to automatically detect abnormal behavior in crowd scene. For this purpose, instead of tracking every person, KLT corners are extracted as feature points to represent moving objects and tracked by optical flow technique to generate motion vectors, which are...
This paper proposes an object-based video recording system, which can track and record the behavior of the moving objects under multiple distributed cameras with non-overlapping views. The object relationship among cameras is trained by using batch-learning procedure, and all probability matrixes are updated constantly for the long-term monitoring. While tracking the object moving across different...
When patients operate a home infusion pump, they maybe make some mistakes, and it will be dangerous. To detect potentially life threatening errors, we design an assistance system based on observation by multiple cameras and robust spatio-temporal algorithm. Firstly, we record the video by multiple cameras when people use the infusion pump. Secondly, we use a robust MoSIFT algorithm, which detects...
In this work, we propose a framework for classifying structured human behavior in complex real environments, where problems such as frequent illumination changes and heavy occlusions are expected. Since target recognition and tracking can be very challenging, we bypass these problems by employing an approach similar to Motion History Images for feature extraction. Furthermore, to tackle outliers residing...
Our research focuses on analysing human activities according to a known behaviorist scenario, in case of noisy and high dimensional collected data. The data come from the monitoring of patients with dementia diseases by wearable cameras. We define a structural model of video recordings based on a Hidden Markov Model. New spatio-temporal features, color features and localization features are proposed...
As a temporal classification problem, visual-based human actions recognition is an important component for some potential applications. In this paper, we combine Fuzzy Principle Component Analysis(Fuzzy PCA) and hidden Conditional Random Fields(HCRFs) to achieve a viewpoint insensitive human action recognition. Fuzzy PCA is used to reduce the dimension of the silhouette image features to obtain the...
This work aims at detecting and tracking vehicles in in-car video. Rather than enhancing shape analysis of various vehicle types and road situations, this work focuses on vehicle and background motions because they are more general than shapes and colors of cars in various road environments. Basic features are tracked stably using corners, intensity peaks, and horizontal line segments. We use the...
This paper presents a computer vision system that analyzes a scene, recognizes human behavior, and produces a semantic interpretation of the recognized behavior. Scene analysis consists of detecting motion and tracking detected objects. Then, the system detects events that define the current behavior of a person. Once behaviors are classified the system recognizes various predefined scenarios. Finally...
We propose a two-stage method for detection of abnormal behaviours, such as aggression and fights in urban environment, which is applicable to operator support in surveillance applications. The proposed method is based on fusion of evidence from audio and optical sensors. In the first stage, a number of modality-specific detectors perform recognition of low-level events. Their outputs act as input...
Human motion recognition is traditionally approached by either recognizing basic motions from features derived from video input or by interpreting complex motions by applying a high-level hierarchy of motion primitives. The former method is usually limited to rather simple motions while the latter requires human expert knowledge to build up a suitable hierarchy. In this paper we propose a new approach...
We present a multi-view three dimensional intelligent surveillance system. We use a multi-agent framework to identify the behaviors of individuals in the scene. Detection and interpretation are performed completely in 3D space. A moving train coach is monitored by eight fish-eye cameras. Segmentation masks extracted from the undistorted images are fed to a distributed 3D reconstruction algorithm producing...
As dissolve is the most common gradual shot transition, dissolve detection plays an important role in video segmentation which is the fundamental step for efficient video indexing and retrieval. However, the existing detection methods easily confuse dissolve with camera motion or object motion when using global features. Besides, when using local features' change tendency, they can' t get accurate...
This paper presents a framework for 3D articulated human body tracking and action classification. The method is based on nonlinear dimensionality reduction of high dimensional data space to low dimensional latent spaces. Human body motion is described by a hierarchy of low dimensional latent spaces which characterize different groups of body parts. We introduce a body pose tracker thats uses the learned...
Object tracking is a crucial task in computer vision systems for surveillance, traffic monitoring or intelligent homes. In all these cases, tracking is based on association of observations. Conventional tracking approaches assume similarity in space, time and appearance of objects in successive observations. However, Observations of objects are often widely separated in time and space when viewed...
Temporal segmentation of human motion into actions is central to the understanding and building of computational models of human motion and activity recognition. Several issues contribute to the challenge of temporal segmentation and classification of human motion. These include the large variability in the temporal scale and periodicity of human actions, the complexity of representing articulated...
Many works of conventional surveillance have focused on people tracking, behavior or event detection, gait or face based recognition, etc. However, role identification is also very important in video surveillance but usually paid less attention. In this paper, we propose a collaborative multi-camera system to identify people with specific roles using a causal network to form a best identification...
This paper presents a real-time tracking system to detect and track multiple moving objects on a controlled pan-tilt camera platform. In order to describe the relationship between the targets and camera in this tracking system, the input/output hidden Markov model (HMM) is applied here in the well-defined spherical camera coordinate. Since the detection and tracking for different targets are performed...
This paper presents a nearly real-time surveillance system to track multiple moving objects by controlling multiple pan-tilt camera platforms. In order to describe the relationship between the targets and camera in this surveillance system, the input/output hidden Markov model (HMM) is applied here in the well-defined spherical camera coordinate. For the less number of cameras to effectively monitor...
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