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290
Fast image-based tracking by selective pixel integration
- In Proceedings of the ICCV Workshop on Frame-Rate Vision
, 1999
"... We provide a fast algorithm to perform image-based tracking, which relies on the selective integration of a small subset of pixels that contain a lot of information about the state variables to be estimated. The resulting dramatic decrease in the number of pixels to process results in a substantial ..."
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Cited by 40 (3 self)
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We provide a fast algorithm to perform image-based tracking, which relies on the selective integration of a small subset of pixels that contain a lot of information about the state variables to be estimated. The resulting dramatic decrease in the number of pixels to process results in a substantial speedup of the basic tracking algorithm. We have used this new method within a surveillance application, where it will enable new capabilities of the system, i.e. real-time, dynamic background subtraction from a panning and tilting camera. 1 Introduction/Philosophical Approach One of the fundamental tasks of real-time processing has been image-based tracking through a video sequence using a parametric motion model. This includes both tracking of a moving object through an image sequence [5] as well as registering of whole images to a reference view to provide software video stabilization
Aerial Video Surveillance and Exploitation
, 2001
"... There is growing interest in performing aerial surveillance using video cameras. Compared to traditional framing cameras, videos provide the capability to observe ongoing activity within a scene and to automatically control the camera to track the activity. However, the high data rates and relativel ..."
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Cited by 39 (1 self)
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There is growing interest in performing aerial surveillance using video cameras. Compared to traditional framing cameras, videos provide the capability to observe ongoing activity within a scene and to automatically control the camera to track the activity. However, the high data rates and relatively small field of view of videos present new technical challenges that must be overcome before videos can be widely used. In this paper, we present a framework and details of the key components for real-time, automatic exploitation of aerial video for surveillance applications. The framework involves separating an aerial video into the natural components corresponding to the scene. Three major components of the scene are the static background geometry, moving objects, and appearance of the static and dynamic components of the scene. In order to delineate videos into these scene components, we have developed real time, image-processing techniques for 2-D/3-D frame-to-frame alignment, change detection, camera control, and tracking of independently moving objects in cluttered scenes. The geo-location of video and tracked objects is estimated by registration of the video to controlled reference imagery, elevation maps, and site models. Finally static, dynamic and reprojected mosaics may be constructed for compression, enhanced visualization, and mapping applications.
Detecting Objects, Shadows and Ghosts in Video Streams by Exploiting Color and Motion Information
- In Proc. of 11th intern conf. on Image Analysis and Processing
, 2001
"... Many approaches to moving object detection for traffic monitoring and video surveillance proposed in the literature are based on background suppression methods. How to correctly and efficiently update the background model and how to deal with shadows are two of the more distinguishing and challengin ..."
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Cited by 38 (0 self)
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Many approaches to moving object detection for traffic monitoring and video surveillance proposed in the literature are based on background suppression methods. How to correctly and efficiently update the background model and how to deal with shadows are two of the more distinguishing and challenging features of such approaches. This work presents a general-purpose method for segmentation of moving visual objects (MVOs) based on an object-level classification in MVOs, ghosts and shadows. Background suppression needs a background model to be estimated and updated: we use motion and shadow information to selectively exclude from the background model MVOs and their shadows, while retaining ghosts. The color information (in the HSV color space) is exploited to shadow suppression and, consequently, to enhance both MVOs segmentation and background update. 1.
Mean Shift And Optimal Prediction For Efficient Object Tracking
- Tracking, International Conference on Image Processing
, 2000
"... A new paradigm for the efficient color-based tracking of objects seen from a moving camera is presented. The proposed technique employs the mean shift analysis to derive the target candidate that is the most similar to a given target model, while the prediction of the next target location is compute ..."
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Cited by 36 (0 self)
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A new paradigm for the efficient color-based tracking of objects seen from a moving camera is presented. The proposed technique employs the mean shift analysis to derive the target candidate that is the most similar to a given target model, while the prediction of the next target location is computed with a Kalman filter. The dissimilarity between the target model and the target candidates is expressed by a metric based on the Bhattacharyya coefficient. The implementation of the new method achieves real-time performance, being appropriate for a large variety of objects with different color patterns. The resulting tracking, tested on various sequences, is robust to partial occlusion, significant clutter, target scale variations, rotations in depth, and changes in camera position. 1.
Tracking People in Presence of Occlusion
- In Asian Conference on Computer Vision
, 2000
"... Tracking humans is a difficult problem because of the non-rigid nature of the human body, and the frequent occlusion encountered among people. We present a framework to track multiple people in a fixed camera situation. Our framework deals implicitly with occlusion, and is able to correctly label pe ..."
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Cited by 35 (0 self)
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Tracking humans is a difficult problem because of the non-rigid nature of the human body, and the frequent occlusion encountered among people. We present a framework to track multiple people in a fixed camera situation. Our framework deals implicitly with occlusion, and is able to correctly label people during occlusion. We first segment a person into classes of similar color using the Expectation Maximization algorithm. Then we use a maximum a posteriori probability approach to track these classes from frame to frame. The system deals well with partial and complete occlusion. Results are presented for an indoor sequence in an office environment. Keywords: human motion analysis, tracking in occlusion, maximum likelihood, expectation maximization, Bayesian probability, activity recognition. 1. INTRODUCTION Tracking moving objects is a key problem in computer vision. Recently there has been a lot of interest in analysis of videos involving humans. Human motion analysis is essential in...
Automatic traffic surveillance system for vehicle tracking and classification
- Proc. IEEE Intelligent Transportation Systems Conference
, 2006
"... This paper presents an automatic traffic surveillance system to estimate important traffic parameters from video sequences using only one camera. Different from traditional methods which can classify vehicles to only cars and non-cars, the proposed method has a good capability to categorize vehicles ..."
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Cited by 31 (0 self)
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This paper presents an automatic traffic surveillance system to estimate important traffic parameters from video sequences using only one camera. Different from traditional methods which can classify vehicles to only cars and non-cars, the proposed method has a good capability to categorize vehicles into more specific classes by introducing a new “linearity” feature in vehicle representation. In addition, the proposed system can well tackle the problem of vehicle occlusions caused by shadows, which often lead to the failure of further vehicle counting and classification. This problem is solved by taking advantages of a line-based shadow algorithm which uses a set of lines to eliminate all unwanted shadows. The used lines are devised from the information of different lane dividing lines. Therefore, an automatic scheme to detect lane dividing lines is also proposed. The found lane dividing lines also can provide important information for feature normalization, which can make the vehicle size more invariant and thus much enhance the accuracy of vehicle classification. Once all features are extracted, an optimal classifier is then designed to robustly categorize vehicles into different classes. When recognizing a vehicle, the designed classifier can collect different evidences from its trajectories and the database to make the best decision for vehicle classification. Since more evidences are used, more robustness of classification can be achieved. Experimental results show that the proposed method is more robust, accurate, and powerful than other traditional methods, which utilize only vehicle size and single frame for classification.
The Sakbot system for moving object detection and tracking
- in Video-Based Surveillance Systems—Computer Vision and Distributed Processing
"... Abstract: This paper presents Sakbot, a system for moving object detection in traffic monitoring and video surveillance applications. The system is endowed with robust and efficient detection techniques, which main features are the statistical and knowledge-based background update and the use of HSV ..."
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Cited by 30 (8 self)
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Abstract: This paper presents Sakbot, a system for moving object detection in traffic monitoring and video surveillance applications. The system is endowed with robust and efficient detection techniques, which main features are the statistical and knowledge-based background update and the use of HSV color information for shadow suppression. Tracking is provided by a symbolic reasoning module allowing flexible object tracking over a variety of different applications. This system proves effective on many different situations, both from the point of view of the scene appearance and the purpose of the application. Key words: motion detection and segmentation, shadow suppression, background differencing, ghost detection 1.
CAMEO: Camera Assisted Meeting Event Observer
"... Static cameras are pervasive in a variety of environments. However it remains a challenging problem to extract and reason about high-level features from real-time and continuous observation of an environment. In this paper, we present CAMEO, the Camera Assisted Meeting Event Observer, which is a phy ..."
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Cited by 29 (17 self)
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Static cameras are pervasive in a variety of environments. However it remains a challenging problem to extract and reason about high-level features from real-time and continuous observation of an environment. In this paper, we present CAMEO, the Camera Assisted Meeting Event Observer, which is a physical awareness system designed for use by an agent-based electronic assistant. CAMEO is an inexpensive high-resolution omnidirectional vision system designed to be used in meeting environments. The multiple camera design achieves the desired high image resolution and lower cost that can be achieved when compared to traditional omnicameras that make use of a single camera and mirror solution.
Panoramic Virtual Stereo Vision of Cooperative Mobile Robots for Localizing 3D Moving Objects
- in Proceedings of the IEEE Workshop on Omnidirectional Vision (OMNIVIS’00), IEEE
, 2000
"... Flexible, reconfigurable vision systems can provide an extremely rich sensing modality for sophisticated multiple robot platforms. We propose a cooperative and adaptive approach of panoramic vision to the problem of finding and protecting humans by a robot team in an emergency circumstance (e.g. a r ..."
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Cited by 28 (7 self)
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Flexible, reconfigurable vision systems can provide an extremely rich sensing modality for sophisticated multiple robot platforms. We propose a cooperative and adaptive approach of panoramic vision to the problem of finding and protecting humans by a robot team in an emergency circumstance (e.g. a rescue in an office building). A panoramic virtual stereo vision method is proposed for this cooperative approach, which features omni-directional visual sensors, cooperative mobile platforms, selected 3D matching, and real-time moving object (people) detection and tracking. The problems of dynamic self-calibration and robust 3D estimation of moving objects are discussed. A careful error analysis of the panoramic stereo triangulation is presented in order to derive rules for optimal view planning. Experimental results are given for detecting and localizing multiple moving objects using two cooperative robot platforms. Keywords: omnidirectional vision, multiple mobile robots, moving object ex...
Multi-target detection and tracking with a laser scanner”, Intelligent Vehicles Symposium
, 2004
"... Abstract — This paper addresses the development of an anti-collision system (ACS) based on a laserscanner, for lowspeed vehicles running in Cybercars scenarios. The ACS core is a multi-target detection and tracking system (MTDATS), which is able to classify several kind of objects and can be easily ..."
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Cited by 28 (6 self)
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Abstract — This paper addresses the development of an anti-collision system (ACS) based on a laserscanner, for lowspeed vehicles running in Cybercars scenarios. The ACS core is a multi-target detection and tracking system (MTDATS), which is able to classify several kind of objects and can be easily expanded to detect new ones. The MTDATS is composed by five modules: 1) scan segmentation; 2) situationbased information integration; 3) object classification using a suitable voting scheme of several object properties; 4) object tracking using a Kalman filter that takes the object type to increase the tracking performance into account; 5) and a database with the objects being tracked at each interval of data processing. For each database object, the time to collision with the vehicle is computed. The worst case time-to-collision and the correspondent predicted impact point on the vehicle, are sent to the path-following controller, which using this information provides collision avoidance behaviour. I.
