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38
Human Body Tracking by Adaptive Background Models and Mean-Shift
"... We present an automatic, real-time human tracking and observation system. Robustness and speed are the two major bottlenecks of the existing approaches. We improve upon the robustness and speed of the current state-of-art by integrating a mean-shift based model update technique with an adaptive chan ..."
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Cited by 41 (4 self)
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We present an automatic, real-time human tracking and observation system. Robustness and speed are the two major bottlenecks of the existing approaches. We improve upon the robustness and speed of the current state-of-art by integrating a mean-shift based model update technique with an adaptive change detection method. We also provide optimal solutions for several other stages including illumination compensation, skin color detection, shadow removal, morphological filtering, event analysis of a tracking system. In addition, we introduce a novel background refresh mechanism. Thus, the proposed framework is capable of handling shortcomings of template and correspondence based tracking approaches. The results with the ICVS-PETS data sets show the effectiveness of the algorithm.
Detection and Classification of Highway Lanes Using Vehicle Motion Trajectories
"... Abstract—Intelligent vision-based traffic surveillance systems are assuming an increasingly important role in highway monitoring and road management schemes. This paper describes a low-level object tracking system that produces accurate vehicle motion trajectories that can be further analyzed to det ..."
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Cited by 33 (0 self)
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Abstract—Intelligent vision-based traffic surveillance systems are assuming an increasingly important role in highway monitoring and road management schemes. This paper describes a low-level object tracking system that produces accurate vehicle motion trajectories that can be further analyzed to detect lane centers and classify lane types. Accompanying techniques for indexing and retrieval of anomalous trajectories are also derived. The predictive trajectory merge-and-split algorithm is used to detect partial or complete occlusions during object motion and incorporates a Kalman filter that is used to perform vehicle tracking. The resulting motion trajectories are modeled using variable low-degree polynomials. A K-means clustering technique on the coefficient space can be used to obtain approximate lane centers. Estimation bias due to vehicle lane changes can be removed using robust estimation techniques based on Random Sample Consensus (RANSAC). Through the use of nonmetric distance functions and a simple directional indicator, highway lanes can be classified into one of the following categories: entry, exit, primary, or secondary. Experimental results are presented to show the real-time application of this approach to multiple views obtained by an uncalibrated pan–tilt–zoom traffic camera monitoring the junction of two busy intersecting highways. Index Terms—Lane detection, motion trajectory, scene interpretation, vehicle tracking. I.
Shadow flow: a recursive method to learn moving cast shadows
- In: Proc international conference on computer vision (ICCV
, 2005
"... Abstract We present a novel algorithm to detect ..."
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Detecting Shadows In Image Sequences
- IN PROC. FIRST EUROPEAN CONFERENCE ON VISUAL MEDIA PRODUCTION LONDON (UK
, 2004
"... In this paper, we present an algorithm for the detection of local illumination changes due to shadows in real world sequences. The algorithm is designed to be able to work when camera, illumination and scene's characteristics are unknown. This feature is highly desirable for a wide range of app ..."
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Cited by 11 (0 self)
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In this paper, we present an algorithm for the detection of local illumination changes due to shadows in real world sequences. The algorithm is designed to be able to work when camera, illumination and scene's characteristics are unknown. This feature is highly desirable for a wide range of applications, such as video production, immersive gaming, and visual surveillance. The algorithm operates as follows. First colour information is exploited, then multiple constraints from physical knowledge are embedded to define the shadow detection algorithm. Colour information is exploited by means of the RGB colour space and by means of photometric invariant features. After colour analysis, a spatio-temporal verification stage is introduced to refine the results. Experimental results show that the proposed algorithm outperforms state-of-the-art methods and can be applied on both indoor and outdoor image sequences.
Moving object segmentation by background subtraction and temporal analysis
, 2004
"... The problem of detecting moving objects is very important in many application contexts such as people detection and recognition, visual surveillance both in indoor and outdoor environments, and so on. In this paper we propose a motion detection algorithm based on background subtraction and shadow re ..."
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Cited by 10 (2 self)
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The problem of detecting moving objects is very important in many application contexts such as people detection and recognition, visual surveillance both in indoor and outdoor environments, and so on. In this paper we propose a motion detection algorithm based on background subtraction and shadow removing. The main idea is to implement a fast and reliable approach for motion detection, able to extract the moving objects without their own shadows, in a single-step algorithm. It is based on the correlation between regions selected from the reference image and the current one. In addition, the proposed algorithm is able to efficiently update the reference model: unlike traditional background updating algorithms, our approach works well on every pixel in the background image, even if covered by a foreground object, in order to have always a consistent reference image. The experiments, performed on real image sequences acquired both in indoor and outdoor environments with natural and artificial lights, demonstrate the effectiveness of the proposed algorithm.
Viewpoint Independent Detection of Vehicle Trajectories and Lane Geometry from Uncalibrated Traffic Surveillance Cameras, accepted ICIAR
- International Conference on Image Analysis and Recognition, Springer, LNCS 3212
, 2004
"... Abstract. In this paper, we present a low-level object tracking system that produces accurate vehicle trajectories and estimates the lane geometry using uncalibrated traffic surveillance cameras. A novel algorithm known as Predictive Trajectory Merge-and-Split (PTMS) has been developed to detect par ..."
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Cited by 8 (2 self)
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Abstract. In this paper, we present a low-level object tracking system that produces accurate vehicle trajectories and estimates the lane geometry using uncalibrated traffic surveillance cameras. A novel algorithm known as Predictive Trajectory Merge-and-Split (PTMS) has been developed to detect partial or complete occlusions during object motion and hence update the number of objects in each tracked blob. This hybrid algorithm is based on the Kalman filter and a set of simple heuristics for temporal analysis. Some preliminary results are presented on the estimation of lane geometry through aggregation and K-means clustering of many individual vehicle trajectories modelled by polynomials of varying degree. We show how this process can be made insensitive to the presence of vehicle lane changes inherent in the data. An advantage of this approach is that estimation of lane geometry can be performed with non-stationary uncalibrated cameras. 1
Motion detection and tracking for an aibo robot using camera motion compensation and kalman filtering
- Lecture Notes in Computer Science 3276 (RoboCup 2004
, 2005
"... Abstract. Motion detection and tracking while moving is a desired ability for any soccer player and in particular for any robot soccer player. For instance, this ability allows the determination of the ball trajectory when the player is moving himself or when he is moving his head, for making or pla ..."
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Cited by 7 (3 self)
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Abstract. Motion detection and tracking while moving is a desired ability for any soccer player and in particular for any robot soccer player. For instance, this ability allows the determination of the ball trajectory when the player is moving himself or when he is moving his head, for making or planning a soccer-play. If a robot soccer player should have a similar functionality, then it requires an algorithm for real-time movement analysis and tracking that can perform well when the camera is moving. The aim of this paper is to propose such an algorithm for an AIBO robot. The proposed algorithm uses motion compensation for having a stabilized background where the movement is detected, and Kalman Filtering for a robust tracking of the moving objects. The algorithm can be adapted for almost any kind of mobile robot. Results of the motion detection and tracking algorithm, working in real-world video sequences, are shown.
Improved shadow removal for robust person tracking in surveillance scenarios
- Proceeding of the 20th International Conference on Pattern Recognition
, 2010
"... Abstract Shadow detection and removal is an important step employed after foreground detection, in order to improve the segmentation of objects for tracking. Methods reported in the literature typically have a significant trade-off between the shadow detection rate (classifying true shadow areas as ..."
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Cited by 6 (2 self)
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Abstract Shadow detection and removal is an important step employed after foreground detection, in order to improve the segmentation of objects for tracking. Methods reported in the literature typically have a significant trade-off between the shadow detection rate (classifying true shadow areas as shadows) and the shadow discrimination rate (discrimination between shadows and foreground). We propose a method that is able to achieve good performance in both cases, leading to improved tracking in surveillance scenarios. Chromacity information is first used to create a mask of candidate shadow pixels, followed by employing gradient information to remove foreground pixels that were incorrectly included in the mask. Experiments on the CAVIAR dataset indicate that the proposed method leads to considerable improvements in multiple object tracking precision and accuracy.
Article FPGA Implementation for Real-Time Background Subtraction Based on Horprasert Model
, 2012
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