Real time recognition of pedestrian and vehicles from videos

Gokul, S.; Kumar, G. Santhosh; Sreeraj, M

doi:10.1145/2393216.2393303

Cited by 2 publications

(1 citation statement)

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“…The moving object detection algorithm with simple background subtraction has been proposed (Sainadh and Arvind 2015). The real time recognition of vehicles and pedestrian from videos using background and edge-based subtraction has proposed (Gokul, Santosh Kumar and Sreeraj 2012). The detection of pedestrians in the video by using motion and shape information has proposed (Ray et al 2016).…”

Section: Literature Reviewmentioning

confidence: 99%

Scalable Pattern Recognition and Real Time Tracking of Moving Objects

2019

Communications and Networking Simulation (CNS 2019)

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This paper proposed a new approach for object tracking and pattern recognition of moving objects during real time video streaming. This approach uses motion based multi-object movement techniques for tracking the objects. Moreover, Spectral clustering with Dynamic Time Warping (DTW) and Naïve Bayes method are used for pattern recognition of tracked objects. This system tracks the moving objects collected as a batch of videos then the pattern recognition technique uses for analyzing vehicles movement to determine normal or abnormal behavior. This paper proposes the tracking algorithm for all moving objects and pattern recognition for only moving vehicles. The performance of tracking trajectories is calculated by finding recall and precision values, which are greater than 95%. The experimental result shows that Naïve Bayes is better than spectral clustering for the classification of vehicle trajectories that conforms Naïve Bayes is an effective tool to scale the pattern recognition of moving vehicles.

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Section: Literature Reviewmentioning

confidence: 99%