Video Object Segmentation and Tracking Framework With Improved Threshold Decision and Diffusion Distance

Chien, Shao-Yi; Chan, Wen S.; Tseng, Yu-Hsiang; Chen, Hong-Yuh

doi:10.1109/tcsvt.2013.2242595

Cited by 33 publications

(12 citation statements)

References 26 publications

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“…Chien et. al [4] proposed a video object recognition and tracking for smart cameras in surveillance networks with robust threshold decision algorithm for video object segmentation with a multi-background model. Another is video object tracking based upon particle filter.…”

Section: Real Time Pedestrian Detection and Tracking For Driver Assismentioning

confidence: 99%

Real Time Pedestrian Detection and Tracking for Driver Assistance Systems

Patil¹,

Arunkumar²

2019

IJEAT

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In Autonomous driving technology detecting pedestrians and vehicles should be fast and efficient in order to avoid accidents. Pedestrian detection and tracking is challenging for complex real world scenes. In proposed system Kalman filter has been used to detect and track the pedestrians. From three frames initially eigen object is computed in video sequences for detection of moving objects, then shape information is used to classify humans and other objects. Moreover with the help of continues multiple frames occlusion between objects get calculated. In the proposed system an application is developed which gives automatic warning in case of doubtful activities performed by pedestrian of zone monitoring which can be used in various domains like defence and traffic monitoring. Proposed algorithm gives accurate moving object detection and advanced sensors are used to detect human movements ahead and alert the driver by using buzzer, result does not affect by body pose of individual

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Section: Real Time Pedestrian Detection and Tracking For Driver Assismentioning

confidence: 99%

Real Time Pedestrian Detection and Tracking for Driver Assistance Systems

Patil¹,

Arunkumar²

2019

IJEAT

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“…However, this model cannot handle strong dynamic back ground and also fails in case of capturing the paused objects. Chien et al [7] proposed a foreground object detection method by using a threshold value. In this article, the authors have assumed that the camera considered for capturing the videos are tolerant to noise and posses a zero -mean Gaussian distribution.…”

Section: Literature Reviewmentioning

confidence: 99%

“…However, most of the background modelling techniques need to combat the challenges due to dynamic or non-static backgrounds, unexpected or steady lighting changes; motion in the object and shade, Background modelling methods should intelligently overcome such issues. To overcome these challenges, many models are presented in the literature [1][2][3][4][5][6][7][8][9][10][11], [13], [15], [16], [17], [18].…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Background Subtraction using Generalized Rayleigh Distribution

Tadiparthi¹,

Yarramalle²,

Vadaparthi³

2018

ijacsa

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Identification of the foreground objects in dynamic scenario video images is an exigent task, when compared to static scenes. In contrast to motionless images, video sequences offer more information concerning how items and circumstances change over time. Pixel based comparisons are carried out to categorize the foreground and the background based on frame difference methodology. In order to have more precise object identification, the threshold value is made static during both the cases, to improve the recognition accuracy, adaptive threshold values are estimated for both the methods. The current article also highlights a methodology using Generalized Rayleigh Distribution (GRD). Experimentation is conducted using benchmark video images and the derived outputs are evaluated using a quantitate approach.

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“…As a consequence of the requirement of an accurate vehicular-target initialization, the automatic tracking of an unknown number of vehicles in a complex scene becomes difficult for the existing stochastic filter-based techniques. In this context, the tracking-by-detection approach has become one of the effective solutions for initializing as well as keeping a moving object within the track ( Chien, Chan, Tseng, & Chen, 2013;Ess et al, 2009;Ess, Schindler, Leibe, & Gool, 2010;Gavrila & Munder, 2007 ). For example, a correct motion model is estimated from a set of candidate trajectories generated by the EKF-trackers of different observations by using the semantic information of the object category ( Ess et al, 2010 ).…”

Section: Introductionmentioning

confidence: 99%