Vehicle Detection in Far Field of View of Video Sequences

2008 ◽  
Vol 2086 (1) ◽  
pp. 23-29
Author(s):  
Itzik Klein ◽  
Tomer Toledo ◽  
Sagi Filin
Keyword(s):  
Vehicle Detection ◽  
Field Of View ◽  
Video Sequences ◽  
Far Field

scholarly journals Implementation of Robust Tracking Algorithm on Nano-Computer

2018 ◽  
Vol 11 (2) ◽  
pp. 446
Author(s):  
Khaled Hammemi ◽  
Mohamed Atri
Keyword(s):  
Video Surveillance ◽  
Visual Servoing ◽  
Active Vision ◽  
Field Of View ◽  
Video Sequences ◽  
Partial Occlusion ◽  
Robust Tracking ◽  
Arm Processor ◽  
Probabilistic Process ◽  
Require Data

<p>In this work, we developed the NSSD-DT method, which allows us to track a target in a robust way. This method effectively overcomes the problems of geometrical deformation of the target, partial occlusion and allows recovery after the target leaves the field of view. The originality of our algorithm is based on a new model, which does not depend on a probabilistic process and does not require data-based beforehand. Experimental results on several difficult video sequences have proven performance benefits. The algorithm is implemented on a BCS 2835 system based on a quad core ARM processor, it is also compared to the software solution. NSSD-DT can be used in several applications such as video surveillance, active vision or industrial visual servoing.</p>

Mosaicing of Confocal Microscopic Video Sequences: Larger Field of View and Still Higher Resolution!

2007 ◽  
Vol 65 (5) ◽  
pp. AB352
Author(s):  
Tom Vercauteren ◽  
Anne Osdoit ◽  
Aymeric Perchant ◽  
Sacha Loiseau
Keyword(s):  
Field Of View ◽  
Video Sequences

scholarly journals Hybrid Video Stabilization for Mobile Vehicle Detection on SURF in Aerial Surveillance

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Gao Chunxian ◽  
Zeng Zhe ◽  
Liu Hui
Keyword(s):  
Vehicle Detection ◽  
Emergency Evacuation ◽  
Video Sequences ◽  
Video Stabilization ◽  
Camera Motion ◽  
Aerial Surveillance ◽  
Low Contrast ◽  
Moving Vehicle ◽  
Mobile Vehicle ◽  
Moving Vehicles

Detection of moving vehicles in aerial video sequences is of great importance with many promising applications in surveillance, intelligence transportation, or public service applications such as emergency evacuation and policy security. However, vehicle detection is a challenging task due to global camera motion, low resolution of vehicles, and low contrast between vehicles and background. In this paper, we present a hybrid method to efficiently detect moving vehicle in aerial videos. Firstly, local feature extraction and matching were performed to estimate the global motion. It was demonstrated that the Speeded Up Robust Feature (SURF) key points were more suitable for the stabilization task. Then, a list of dynamic pixels was obtained and grouped for different moving vehicles by comparing the different optical flow normal. To enhance the precision of detection, some preprocessing methods were applied to the surveillance system, such as road extraction and other features. A quantitative evaluation on real video sequences indicated that the proposed method improved the detection performance significantly.

scholarly journals Vehicle detection and tracking for traffic management

2021 ◽  
Vol 10 (1) ◽  
pp. 66
Author(s):  
Mallikarjun Anandhalli ◽  
Vishwanth P. Baligar ◽  
Pavana Baligar ◽  
Pooja Deepsir ◽  
Mithali Iti
Keyword(s):  
Traffic Management ◽  
Vehicle Detection ◽  
Research Area ◽  
Tracking Algorithm ◽  
Video Sequences ◽  
Detection And Tracking ◽  
Vehicle Detection And Tracking ◽  
Corner Points ◽  
Computer Research

<span lang="EN-US">The detection of object with respect to Vehicle and tracking is the most needed step in computer research area as there is wide investment being made form Intelligent Traffic Management. Due to changes in vision or scenes, detection and tracking of vehicle under different drastic conditions has become most challenging process because of the illumination, shadows etc. To overcome this, we propose a method which uses TensorFlow fused with corner points of the vehicles for detection of vehicle and tracking of an vehicle is formulated again, the location of the object which is detected is passed to track the detected object, using the tracking algorithm based on CNN. The proposed algorithm gives result of 90.88% accuracy of detection in video sequences under different conditions of climate.</span>

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