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60
Learning Object Motion Patterns for Anomaly Detection and Improved Object Detection
"... We present a novel framework for learning patterns of motion and sizes of objects in static camera surveillance. The proposed method provides a new higher-level layer to the traditional surveillance pipeline for anomalous event detection and scene model feedback. Pixel level probability density func ..."
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Cited by 35 (2 self)
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We present a novel framework for learning patterns of motion and sizes of objects in static camera surveillance. The proposed method provides a new higher-level layer to the traditional surveillance pipeline for anomalous event detection and scene model feedback. Pixel level probability density functions (pdfs) of appearance have been used for background modelling in the past, but modelling pixel level pdfs of object speed and size from the tracks is novel. Each pdf is modelled as a multivariate Gaussian Mixture Model (GMM) of the motion (destination location & transition time) and the size (width & height) parameters of the objects at that location. Output of the tracking module is used to perform unsupervised EM-based learning of every GMM. We have successfully used the proposed scene model to detect local as well as global anomalies in object tracks. We also show the use of this scene model to improve object detection through pixel-level parameter feedback of the minimum object size and background learning rate. Most object path modelling approaches first cluster the tracks into major paths in the scene, which can be a source of error. We avoid this by building local pdfs that capture a variety of tracks which are passing through them. Qualitative and quantitative analysis of actual surveillance videos proved the effectiveness of the proposed approach. 1.
Background Modeling using Mixture of Gaussians for Foreground Detection – A survey
- Recent Patents on Computer Science
, 2008
"... Abstract: Mixture of Gaussians is a widely used approach for background modeling to detect moving objects from static cameras. Numerous improvements of the original method developed by Stauffer and Grimson [1] have been proposed over the recent years and the purpose of this paper is to provide a sur ..."
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Cited by 33 (2 self)
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Abstract: Mixture of Gaussians is a widely used approach for background modeling to detect moving objects from static cameras. Numerous improvements of the original method developed by Stauffer and Grimson [1] have been proposed over the recent years and the purpose of this paper is to provide a survey and an original classification of these improvements. We also discuss relevant issues to reduce the computation time. Firstly, the original MOG are reminded and discussed following the challenges met in video sequences. Then, we categorize the different improvements found in the literature. We have classified them in term of strategies used to improve the original MOG and we have discussed them in term of the critical situations they claim to handle. After analyzing the strategies and identifying their limitations, we conclude with several promising directions for future research.
M.: Attribute-based people search in surveillance environments
- In: IEEE Workshop on Applications of Computer Vision
, 2009
"... We propose a novel framework for searching for people in surveillance environments. Rather than relying on face recognition technology, which is known to be sensitive to typical surveillance conditions such as lighting changes, face pose variation, and low-resolution imagery, we approach the problem ..."
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Cited by 33 (4 self)
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We propose a novel framework for searching for people in surveillance environments. Rather than relying on face recognition technology, which is known to be sensitive to typical surveillance conditions such as lighting changes, face pose variation, and low-resolution imagery, we approach the problem in a different way: we search for people based on a parsing of human parts and their attributes, including facial hair, eyewear, clothing color, etc. These attributes can be extracted using detectors learned from large amounts of training data. A complete system that implements our framework is presented. At the interface, the user can specify a set of personal characteristics, and the system then retrieves events that match the provided description. For example, a possible query is “show me the bald people who entered a given building last Saturday wearing a red shirt and sunglasses. ” This capability is useful in several applications, such as finding suspects or missing people. To evaluate the performance of our approach, we present extensive experiments on a set of images collected from the Internet, on infrared imagery, and on two-and-ahalf months of video from a real surveillance environment. We are not aware of any similar surveillance system capable of automatically finding people in video based on their fine-grained body parts and attributes. 1.
Autonomous Learning of a Robust Background Model for Change Detection
, 2006
"... We propose a framework for observing static scenes that can be used to detect unknown objects (i.e., left luggage or lost cargo) as well as objects that were removed or changed (i.e., theft or vandalism). The core of the method is a robust background model based on on-line AdaBoost which is able to ..."
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Cited by 12 (3 self)
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We propose a framework for observing static scenes that can be used to detect unknown objects (i.e., left luggage or lost cargo) as well as objects that were removed or changed (i.e., theft or vandalism). The core of the method is a robust background model based on on-line AdaBoost which is able to adapt to a large variety of appearance changes (e.g., blinking lights, illumination changes). However, a natural scene contains foreground objects (e.g., persons, cars). Thus, a detector for these foreground objects is automatically trained and a tracker is initialized for two purposes: (1) to prevent that a foreground object is included into the background model and (2) to analyze the scene. For efficiency reasons it is important that all components of the framework are using the same efficient data structure. We demonstrate and evaluate the developed method on the PETS 2006 sequences as well as on own sequences of surveillance cameras.
Background subtraction on distributions
- In ECCV
, 2008
"... Abstract. Environmental monitoring applications present a challenge to current background subtraction algorithms that analyze the temporal variability of pixel intensities, due to the complex texture and motion of the scene. They also present a challenge to segmentation algorithms that compare inten ..."
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Cited by 12 (2 self)
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Abstract. Environmental monitoring applications present a challenge to current background subtraction algorithms that analyze the temporal variability of pixel intensities, due to the complex texture and motion of the scene. They also present a challenge to segmentation algorithms that compare intensity or color distributions between the foreground and the background in each image independently, because objects of interest such as animals have adapted to blend in. Therefore, we have developed a background modeling and subtraction scheme that analyzes the temporal variation of intensity or color distributions, instead of either looking at temporal variation of point statistics, or the spatial variation of region statistics in isolation. Distributional signatures are less sensitive to movements of the textured background, and at the same time they are more robust than individual pixel statistics in detecting foreground objects. They also enable slow background update, which is crucial in monitoring applications where processing power comes at a premium, and where foreground objects, when present, may move less than the background and therefore disappear into it when a fast update scheme is used. Our approach compares favorably with the state of the art both in generic lowlevel detection metrics, as well as in application-dependent criteria. 1
Searching surveillance video
- in IEEE Conference on Advanced Video and Signal Based Surveillance (AVSS 2007
, 2007
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Large-Scale Vehicle Detection in Challenging Urban Surveillance Environments
"... 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 virt ..."
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Cited by 9 (5 self)
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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 laptop computer. Our proposed approach relies on three key contributions: 1) a co-training scheme where data is automatically captured based on motion and shape cues and used to train a detector based on appearance information; 2) an occlusion handling technique based on synthetically generated training samples obtained through Poisson image reconstruction from image gradients; 3) massively parallel feature selection over multiple feature planes which allows the final detector to be more accurate and more efficient. We perform a comprehensive quantitative analysis to validate our approach, showing its usefulness in realistic urban surveillance settings. 1.
Real-time human posture reconstruction in wireless smart camera networks
- In Proceedings of the International Conference on Information Processing in Sensor Networks
, 2008
"... While providing a variety of intriguing application op-portunities, a vision sensor network poses three key chal-lenges. High computation capacity is required for early vi-sion functions to enable real-time performance. Wireless links limit image transmission in the network due to both bandwidth and ..."
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Cited by 8 (0 self)
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While providing a variety of intriguing application op-portunities, a vision sensor network poses three key chal-lenges. High computation capacity is required for early vi-sion functions to enable real-time performance. Wireless links limit image transmission in the network due to both bandwidth and energy concerns. Last but not least, there is a lack of established vision-based fusion mechanisms when a network of cameras is available. In this paper a distrib-uted vision processing implementation of human pose inter-pretation on a wireless smart camera network is presented. The motivation for employing distributed processing is to both achieve real-time vision and provide scalability for de-veloping more complex vision algorithms. The distributed processing operation includes two levels. One is that each smart camera processes its local vision data, achieving spa-tial parallelism. The other is that different functionalities of the whole line of vision processing are assigned to early vi-sion and object-level processors, achieving functional par-allelism based on the processor capabilities. Aiming for low power consumption and high image processing perfor-mance, the wireless smart camera is based on an SIMD (single-instruction multiple-data) video analysis processor, an 8051 micro-controller as the local host, and wireless communication through the IEEE 802.15.4 standard. The vision algorithm implements 3D human pose reconstruc-tion. From the live image data from the sensor the smart camera acquires critical joints of the subject in the scene through local processing. The results obtained by multi-ple smart cameras are then transmitted through the wireless channel to a central PC where the 3D pose is recovered and demonstrated in a virtual reality gaming application. The system operates in real time with a 30 frames/sec rate. 1.
Cast shadow removal with gmm for surface reflectance component
- In Proceedings of the IEEE International Conference on Pattern Recognition
, 2006
"... Cast shadow on the background is generated by an object moving between a light source and the background. The position and illumination of the source always change with time, while the background is stable. Therefore, features connected with light source always change with time, such as geometry and ..."
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Cited by 7 (4 self)
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Cast shadow on the background is generated by an object moving between a light source and the background. The position and illumination of the source always change with time, while the background is stable. Therefore, features connected with light source always change with time, such as geometry and color. In this paper, we present a shadow removal method by homomorphic model to extract surface reflectance component, which is only connected with background of the scene and is robust to change of light source. We assume that reflectance component fits Gaussian distribution, and then use GMM to model it. Experimental results show that, except dealing with shadow, our method is not sensitive to the change of illumination.
Robust people detection and tracking in a multi-camera indoor visual surveillance system
- ICME 2007
, 2007
"... In this paper we describe the analysis component of an indoor, real-time, multi-camera surveillance system. The analysis includes: (1) a novel feature-level foreground segmentation method which achieves efficient and reliable segmentation results even under complex conditions, (2) an efficient greed ..."
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Cited by 6 (4 self)
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In this paper we describe the analysis component of an indoor, real-time, multi-camera surveillance system. The analysis includes: (1) a novel feature-level foreground segmentation method which achieves efficient and reliable segmentation results even under complex conditions, (2) an efficient greedy search based approach for tracking multiple people through occlusion, and (3) a method for multicamera handoff that associates individual trajectories in adjacent cameras. The analysis is used for an 18 camera surveillance system that has been running continuously in an indoor business over the past several months. Our experiments demonstrate that the processing method for people detection and tracking across multiple cameras is fast and robust. 1.
