581 research outputs found
Shapes-from-silhouettes based 3D reconstruction for athlete evaluation during exercising
Shape-from-silhouettes is a very powerful tool to create a 3D reconstruction of an object using a limited number of cameras which are all facing an overlapping area. Synchronously captured video frames add the possibility of 3D reconstruction on a frame-by-frame-basis making it possible to watch movements in 3D. This 3D model can be viewed from any direction and therefore adds a lot of information for both athletes and coaches
A Bayesian Approach on People Localization in Multicamera Systems
In this paper we introduce a Bayesian approach on multiple people localization in multi-camera systems. First, pixel-level features are extracted, which are based on physical properties of the 2-D image formation process, and provide information about the head and leg positions of the pedestrians, distinguishing standing and walking people, respectively. Then features from the multiple camera views are fused to create evidence for the location and height of people in the ground plane. This evidence accurately estimates the leg position even if either the area of interest is only a part of the scene, or the overlap ratio of the silhouettes from irrelevant outside motions with the monitored area is significant. Using this information we create a 3-D object configuration model in the real world. We also utilize a prior geometrical constraint, which describes the possible interactions between two pedestrians. To approximate the position of the people, we use a population of 3-D cylinder objects, which is realized by a Marked Point Process. The final configuration results are obtained by an iterative stochastic energy optimization algorithm. The proposed approach is evaluated on two publicly available datasets, and compared to a recent state-of-the-art technique. To obtain relevant quantitative test results, a 3-D Ground Truth annotation of the real pedestrian locations is prepared, while two different error metrics and various parameter settings are proposed and evaluated, showing the advantages of our proposed model
Automatic visual detection of human behavior: a review from 2000 to 2014
Due to advances in information technology (e.g., digital video cameras, ubiquitous sensors), the automatic detection of human behaviors from video is a very recent research topic. In this paper, we perform a systematic and recent literature review on this topic, from 2000 to 2014, covering a selection of 193 papers that were searched from six major scientific publishers. The selected papers were classified into three main subjects: detection techniques, datasets and applications. The detection techniques were divided into four categories (initialization, tracking, pose estimation and recognition). The list of datasets includes eight examples (e.g., Hollywood action). Finally, several application areas were identified, including human detection, abnormal activity detection, action recognition, player modeling and pedestrian detection. Our analysis provides a road map to guide future research for designing automatic visual human behavior detection systems.This work is funded by the Portuguese Foundation for Science and Technology (FCT - Fundacao para a Ciencia e a Tecnologia) under research Grant SFRH/BD/84939/2012
Zernike velocity moments for sequence-based description of moving features
The increasing interest in processing sequences of images motivates development of techniques for sequence-based object analysis and description. Accordingly, new velocity moments have been developed to allow a statistical description of both shape and associated motion through an image sequence. Through a generic framework motion information is determined using the established centralised moments, enabling statistical moments to be applied to motion based time series analysis. The translation invariant Cartesian velocity moments suffer from highly correlated descriptions due to their non-orthogonality. The new Zernike velocity moments overcome this by using orthogonal spatial descriptions through the proven orthogonal Zernike basis. Further, they are translation and scale invariant. To illustrate their benefits and application the Zernike velocity moments have been applied to gait recognitionâan emergent biometric. Good recognition results have been achieved on multiple datasets using relatively few spatial and/or motion features and basic feature selection and classification techniques. The prime aim of this new technique is to allow the generation of statistical features which encode shape and motion information, with generic application capability. Applied performance analyses illustrate the properties of the Zernike velocity moments which exploit temporal correlation to improve a shape's description. It is demonstrated how the temporal correlation improves the performance of the descriptor under more generalised application scenarios, including reduced resolution imagery and occlusion
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Video content analysis for automated detection and tracking of humans in CCTV surveillance applications
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.The problems of achieving high detection rate with low false alarm rate for human detection and tracking in video sequence, performance scalability, and improving response time are addressed in this thesis. The underlying causes are the effect of scene complexity, human-to-human interactions, scale changes, and scene background-human interactions. A two-stage processing solution, namely, human detection, and human tracking with two novel pattern classifiers is presented. Scale independent human detection is achieved by processing in the wavelet domain using square wavelet features. These features used to characterise human silhouettes at different scales are similar to rectangular features used in [Viola 2001]. At the detection stage two detectors are combined to improve detection rate. The first detector is based on shape-outline of humans extracted from the scene using a reduced complexity outline extraction algorithm. A Shape mismatch measure is used to differentiate between the human and the background class. The second detector uses rectangular features as primitives for silhouette description in the wavelet domain. The marginal distribution of features collocated at a particular position on a candidate human (a patch of the image) is used to describe statistically the silhouette. Two similarity measures are computed between a candidate human and the model histograms of human and non human classes. The similarity measure is used to discriminate between the human and the non human class. At the tracking stage, a tracker based on joint probabilistic data association filter (JPDAF) for data association, and motion correspondence is presented. Track clustering is used to reduce hypothesis enumeration complexity. Towards improving response time with increase in frame dimension, scene complexity, and number of channels; a scalable algorithmic architecture and operating accuracy prediction technique is presented. A scheduling strategy for improving the response time and throughput by parallel processing is also presented
Moving object detection, tracking and classification for smart video surveillance
Cataloged from PDF version of article.Video surveillance has long been in use to monitor security sensitive areas such
as banks, department stores, highways, crowded public places and borders. The
advance in computing power, availability of large-capacity storage devices and
high speed network infrastructure paved the way for cheaper, multi sensor video
surveillance systems. Traditionally, the video outputs are processed online by
human operators and are usually saved to tapes for later use only after a forensic
event. The increase in the number of cameras in ordinary surveillance systems
overloaded both the human operators and the storage devices with high volumes
of data and made it infeasible to ensure proper monitoring of sensitive areas for
long times. In order to filter out redundant information generated by an array of
cameras, and increase the response time to forensic events, assisting the human
operators with identification of important events in video by the use of âsmartâ
video surveillance systems has become a critical requirement. The making of
video surveillance systems âsmartâ requires fast, reliable and robust algorithms
for moving object detection, classification, tracking and activity analysis.
In this thesis, a smart visual surveillance system with real-time moving object
detection, classification and tracking capabilities is presented. The system
operates on both color and gray scale video imagery from a stationary camera.
It can handle object detection in indoor and outdoor environments and under
changing illumination conditions. The classification algorithm makes use of the
shape of the detected objects and temporal tracking results to successfully categorize
objects into pre-defined classes like human, human group and vehicle.
The system is also able to detect the natural phenomenon fire in various scenes
reliably. The proposed tracking algorithm successfully tracks video objects even
in full occlusion cases. In addition to these, some important needs of a robust smart video surveillance system such as removing shadows, detecting sudden illumination
changes and distinguishing left/removed objects are met.DedeoÄlu, YiÄithanM.S
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