Segmentation and tracking of multiple video objects

Colombari, A.; Fusiello, Andrea; Murino, Vittorio

doi:10.1016/j.patcog.2006.07.008

Cited by 42 publications

(21 citation statements)

References 16 publications

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“…Liyuan Li et al [7] present a Bayesian framework that incorporates spectral, spatial, and temporal features to characterize the background appearance and to detect foreground objects from complex environments, e.g., subway stations, campuses, and sidewalks. A. Colombari et al [8] describe a technique that produces a content-based representation of a video shot for multiple moving objects, including object segmentation based on ego-motion compensation, background modeling using tools from robust statistics, and region matching based on the Mahalanobis distance.…”

Section: Literature Reviewmentioning

confidence: 99%

Traffic Flow Detection for Complex Scene Based on Image Sequence

Li¹,

Feng²,

Pan³

et al. 2014

Proceedings of the 2014 International Conference on Mechatronics, Control and Electronic Engineering

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Abstract-A method of traffic flow detection for complex scenes based on image sequence is proposed, which is aiming at realizing intelligent vehicle detection and flow statistics for traffic videos shot by single camera located at the urban traffic intersection. For target vehicle detection, Gaussianfiltering mean method is utilized to create the dynamic realtime background; meanwhile it is combined with frame difference method to locate the motion vehicles in the foreground. Moreover, test-stripe detecting method is used for vehicle counting, in which data streams representing vehicle information are extracted with sliding window and are modified by Predictor-Corrector scheme to count the vehicles more accurately. Finally, a prototype system is designed to realize vehicle flow statistics for complex traffic scenes, combined with the judgment algorithm of vehicle traveling direction. Experimental simulation represents that target vehicle detection avoids the respective disadvantages of the two methods, that is background difference method and frame difference method, and obtains better detection effects. The prototype system captures good real-time performance and statistical data of the vehicle flow are comparatively accurate.

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Section: Literature Reviewmentioning

confidence: 99%

Traffic Flow Detection for Complex Scene Based on Image Sequence

Li¹,

Feng²,

Pan³

et al. 2014

Proceedings of the 2014 International Conference on Mechatronics, Control and Electronic Engineering

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“…Indeed the detection of moving objects as a shadows pixels allows to an over segmentation which will damage many works where this paper is registered. This algorithm is extremely important because it is a part of player's classification and tracking [15] on a football scenes.…”

Section: Svd Image Approximationmentioning

confidence: 99%

Objects Detection by Singular Value Decomposition Technique in Hybrid Color Space: Application to Football Images

Jlassi¹,

Douik²,

Messaoud³

2010

INT J COMPUT COMMUN

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Abstract:In this paper, we present an improvement non-parametric background modeling and foreground segmentation. This method is important; it gives the hand to check many states kept by each background pixel. In other words, generates the historic for each pixel, indeed on certain computer vision applications the background can be dynamic; several intensities were projected on the same pixel. This paper describe a novel approach which integrate both Singular Value Decomposition (SVD) of each image to increase the compactness density distribution and hybrid color space suitable to this case constituted by the three relevant chromatics levels deduced by histogram analysis. In fact the proposed technique presents the efficiency of SVD and color information to subtract background pixels corresponding to shadows pixels. This method has been applied on colour images issued from soccer video. In the other hand to achieve some statistics information about players ongoing of the match (football, handball, volley ball, Rugby...) as well as to refine their strategy coach and leaders need to have a maximum of technical-tactics information. For this reason it is prominent to elaborate an algorithm detecting automatically interests color regions (players) and solve the confusion problem between background and foreground every moment from images sequence.

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“…The moving objects get removed in the final mosaic by computing the median of the grey levels. A. Colombari et al [7] achieved segmenting and connecting one or more foreground moving objects. H Li and J Tang et al [8] automatically segmented the highlights in the video clips and recognized the action types to support actionbased video indexing and retrieval.…”

Section: Introductionmentioning

confidence: 99%

Representation of the player action in sport videos

Zhang

Qiu

Wang

et al. 2013

2013 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference

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In a sport video, the complex and dynamic background and the complexity of player actions bring considerable difficulty to analyze the player's action. In this paper we propose a method to represent the player's action on the panoramic background in a whole. It is helpful for watching and analyzing the player's action. We first build the panoramic background by the inter-frame relationship. Then object segmentation is implemented by subtraction between the panoramic background and each warped frame. Finally, the player's action in each frame is represented on the panoramic background. And the trajectory of the player action is presented on the background. The experimental results show that the proposed method is effective for the sports video like diving, jumps, which the player performs his or her action in a large arena, and the camera motion mainly includes horizontal and vertical direction.

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Segmentation and tracking of multiple video objects

Cited by 42 publications

References 16 publications

Traffic Flow Detection for Complex Scene Based on Image Sequence

Traffic Flow Detection for Complex Scene Based on Image Sequence

Objects Detection by Singular Value Decomposition Technique in Hybrid Color Space: Application to Football Images

Representation of the player action in sport videos

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