Dynamic Shadow Detection and Removal for Vehicle Tracking System

2021 ◽  
Author(s):  
Kalpesh R. Jadav ◽  
Arvind R. Yadav
Keyword(s):  
Detection Rate ◽  
Video Sequence ◽  
State Of The Art ◽  
Tracking System ◽  
Shadow Detection ◽  
Vehicle Tracking ◽  
Video Sequences ◽  
Frame Difference ◽  
Discrimination Rate ◽  
High Discrimination

Shadow leads to failure of moving target positioning, segmentation, tracking, and classification in the video surveillance system thus shadow detection and removal is essential for further computer vision process. The existing state-of-the-art methods for dynamic shadow detection have produced a high discrimination rate but a poor detection rate (foreground pixels are classified as shadow pixels). This paper proposes an effective method for dynamic shadow detection and removal based on intensity ratio along with frame difference, gamma correction, and morphology operations. The performance of the proposed method has been tested on two outdoor ATON datasets, namely, highway-I and highway-III for vehicle tracking systems. The proposed method has produced a discrimination rate of 89.07% and a detection rate of 80.79% for highway-I video sequences. Similarly, for a highway-III video sequence, the discrimination rate of 85.60% and detection rate of 84.05% have been obtained. Investigational outcomes show that the proposed method is the simple, steadiest, and robust for dynamic shadow detection on the dataset used in this work.

Robust Feature Points Extraction Based on Harris and SIFT

2013 ◽  
Vol 347-350 ◽  
pp. 3500-3504
Author(s):  
Xiao Ran Guo ◽  
Shao Hui Cui ◽  
Fang Dan
Keyword(s):  
Video Sequence ◽  
State Of The Art ◽  
Image Motion ◽  
Video Sequences ◽  
Stabilization System ◽  
Feature Points ◽  
Image Stabilization ◽  
Current State ◽  
Novel Approach ◽  
Key Points

This article presents a novel approach to extract robust local feature points of video sequence in digital image stabilization system. Robust Harris-SIFT detector is proposed to select the most stable SIFT key points in the video sequence where image motion is happened due to vehicle or platform vibration. Experimental results show that the proposed scheme is robust to various transformations of video sequences, such as translation, rotation and scaling, as well as blurring. Compared with the current state-of-the-art schemes, the proposed scheme yields better performances.

scholarly journals HYBRID APPROACH FOR KEY FRAME EXTRACTION FROM VIDEO SEQUENCE

2017 ◽  
Vol 5 (4RACSIT) ◽  
pp. 97-104
Author(s):  
Satish Kumar
Keyword(s):  
Background Subtraction ◽  
Video Sequence ◽  
State Of The Art ◽  
Hybrid Approach ◽  
Considerable Improvement ◽  
Video Sequences ◽  
Key Frame ◽  
Key Frames ◽  
Mixture Of Gaussian ◽  
Fine Tune

This paper proposed and developed hybrid approach for extraction of key-frames from video sequences from stationary camera. This method first uses histogram difference to extract the candidate key frames from the video sequences, later using Background subtraction algorithm (Mixture of Gaussian) was used to fine tune the final key frames from the video sequences. This developed approach show considerable improvement over the state-of-the art techniques and same is reported in this paper.

scholarly journals FUTUREGAN: ANTICIPATING THE FUTURE FRAMES OF VIDEO SEQUENCES USING SPATIO-TEMPORAL 3D CONVOLUTIONS IN PROGRESSIVELY GROWING GANS

2019 ◽  
Vol XLII-2/W16 ◽  
pp. 3-11 ◽  
Author(s):  
S. Aigner ◽  
M. Körner
Keyword(s):  
Video Sequence ◽  
State Of The Art ◽  
Input Sequence ◽  
Video Sequences ◽  
Complex Task ◽  
High Quality ◽  
Spatio Temporal ◽  
Resolution Single ◽  
Additional Constraints ◽  
Video Prediction

<p><strong>Abstract.</strong> We introduce a new <i>encoder-decoder GAN</i> model, <i>FutureGAN</i>, that predicts future frames of a video sequence conditioned on a sequence of past frames. During training, the networks solely receive the raw pixel values as an input, without relying on additional constraints or dataset specific conditions. To capture both the spatial and temporal components of a video sequence, spatio-temporal 3d convolutions are used in all encoder and decoder modules. Further, we utilize concepts of the existing <i>progressively growing GAN (PGGAN)</i> that achieves high-quality results on generating high-resolution single images. The FutureGAN model extends this concept to the complex task of video prediction. We conducted experiments on three different datasets, <i>MovingMNIST</i>, <i>KTH Action</i>, and <i>Cityscapes</i>. Our results show that the model learned representations to transform the information of an input sequence into a plausible future sequence effectively for all three datasets. The main advantage of the FutureGAN framework is that it is applicable to various different datasets without additional changes, whilst achieving stable results that are competitive to the state-of-the-art in video prediction. The code to reproduce the results of this paper is publicly available at https://github.com/TUM-LMF/FutureGAN.</p>

scholarly journals Contiguous Loss for Motion-Based, Non-Aligned Image Deblurring

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 630
Author(s):  
Wenjia Niu ◽  
Kewen Xia ◽  
Yongke Pan
Keyword(s):  
Video Sequence ◽  
State Of The Art ◽  
Video Sequences ◽  
Dynamic Scenes ◽  
Multiple Objects ◽  
Proposed Model ◽  
Ill Posed ◽  
Spatio Temporal ◽  
Better Than ◽  
General Dynamic

In general dynamic scenes, blurring is the result of the motion of multiple objects, camera shaking or scene depth variations. As an inverse process, deblurring extracts a sharp video sequence from the information contained in one single blurry image—it is itself an ill-posed computer vision problem. To reconstruct these sharp frames, traditional methods aim to build several convolutional neural networks (CNN) to generate different frames, resulting in expensive computation. To vanquish this problem, an innovative framework which can generate several sharp frames based on one CNN model is proposed. The motion-based image is put into our framework and the spatio-temporal information is encoded via several convolutional and pooling layers, and the output of our model is several sharp frames. Moreover, a blurry image does not have one-to-one correspondence with any sharp video sequence, since different video sequences can create similar blurry images, so neither the traditional pixel2pixel nor perceptual loss is suitable for focusing on non-aligned data. To alleviate this problem and model the blurring process, a novel contiguous blurry loss function is proposed which focuses on measuring the loss of non-aligned data. Experimental results show that the proposed model combined with the contiguous blurry loss can generate sharp video sequences efficiently and perform better than state-of-the-art methods.

Vehicle Tracking System Using GPS

2012 ◽  
Vol 2 (5) ◽  
pp. 104-105
Author(s):  
A. Jayanth A. Jayanth ◽  
◽  
S. Hemachandra S. Hemachandra ◽  
B. Suneetha B. Suneetha ◽  
B. Gowri Prasad B. Gowri Prasad
Keyword(s):  
Tracking System ◽  
Vehicle Tracking ◽  
Vehicle Tracking System

eSOS® – emergency Sanitation Operation System

2014 ◽  
Vol 5 (1) ◽  
pp. 156-164 ◽  
Author(s):  
D. Brdjanovic ◽  
F. Zakaria ◽  
P. M. Mawioo ◽  
H. A. Garcia ◽  
C. M. Hooijmans ◽  
...  
Keyword(s):  
Business Model ◽  
Management System ◽  
Tracking System ◽  
The Philippines ◽  
Vehicle Tracking ◽  
Treatment Facility ◽  
Operation System ◽  
System A ◽  
Coordination Center ◽  
Vehicle Tracking System

This paper presents the innovative emergency Sanitation Operation System (eSOS) concept created to improve the entire emergency sanitation chain and provide decent sanitation to people in need. The eSOS kit is described including its components: eSOS smart toilets, an intelligent excreta collection vehicle-tracking system, a decentralized excreta treatment facility, an emergency sanitation coordination center, and an integrated eSOS communication and management system. The paper further deals with costs and the eSOS business model, its challenges, applicability and relevance. The first application, currently taking place in the Philippines will bring valuable insights on the future of the eSOS smart toilet. It is expected that eSOS will bring changes to traditional disaster relief management.

Vehicle tracking on video sequences via subspace learning

2018 ◽  
Author(s):  
Abdulhakim Gultekin ◽  
Ismail Can Buyuktepe ◽  
Hasan Huseyin Sonmez ◽  
Ali Koksal Hocaoglu
Keyword(s):  
Subspace Learning ◽  
Vehicle Tracking ◽  
Video Sequences
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