Security System and Surveillance Using Real Time Object Tracking and Multiple Cameras

2011 ◽  
Vol 403-408 ◽  
pp. 4968-4973
Author(s):  
Rajendra Kachhava ◽  
Vivek Srivastava ◽  
Rajkumar Jain ◽  
Ekta Chaturvedi
Keyword(s):  
Object Detection ◽  
Object Tracking ◽  
Real Time ◽  
Video Surveillance ◽  
Research Field ◽  
Security System ◽  
Multiple Cameras ◽  
Multiple Camera ◽  
Detection And Tracking ◽  
Real Time Tracking

In this paper we propose multiple cameras using real time tracking for surveillance and security system. It is extensively used in the research field of computer vision applications, like that video surveillance, authentication systems, robotics, pre-stage of MPEG4 image compression and user inter faces by gestures. The key components of tracking for surveillance system are extracting the feature, background subtraction and identification of extracted object. Video surveillance, object detection and tracking have drawn a successful increased interest in recent years. A object tracking can be understood as the problem of finding the path (i.e. trajectory) and it can be defined as a procedure to identify the different positions of the object in each frame of a video. Based on the previous work on single detection using single stationary camera, we extend the concept to enable the tracking of multiple object detection under multiple camera and also maintain a security based system by multiple camera to track person in indoor environment, to identify by my proposal system which consist of multiple camera to monitor a person. Present study mainly aims to provide security and detect the moving object in real time video sequences and live video streaming. Based on a robust algorithm for human body detection and tracking in videos created with support of multiple cameras.

A Multi-Object Tracking Algorithm Based on Multi-Camera

2011 ◽  
Vol 135-136 ◽  
pp. 70-75
Author(s):  
Ming Xin Jiang ◽  
Hong Yu Wang ◽  
Chao Lin
Keyword(s):  
Computer Vision ◽  
Object Tracking ◽  
Real Time ◽  
Tracking Algorithm ◽  
Location Information ◽  
Multiple Cameras ◽  
Multiple Objects ◽  
Vanishing Points ◽  
Basic Aspect ◽  
Real Time Tracking

As a basic aspect of computer vision, reliable tracking of multiple objects is still an open and challenging issue for both theory studies and real applications. A novel multi-object tracking algorithm based on multiple cameras is proposed in this paper. We obtain the foreground likelihood maps in each view by modeling the background using the codebook algorithm. The view-to-view homographies are computed using several landmarks on the chosen plane. Then, we achieve the location information of multi-target at chest layer and realize the tracking task. The proposed algorithm does not require detecting the vanishing points of cameras, which reduces the complexity and improves the accuracy of the algorithm. The experimental results show that our method is robust to the occlusion and could satisfy the real-time tracking requirement.

scholarly journals Deep Learning-Based Real-Time Multiple-Object Detection and Tracking from Aerial Imagery via a Flying Robot with GPU-Based Embedded Devices

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3371 ◽  
Author(s):  
Hossain ◽  
Lee
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Real Time ◽  
Moving Objects ◽  
State Of The Art ◽  
Target Position ◽  
Guidance System ◽  
Aerial Imagery ◽  
Detection And Tracking ◽  
Real Time Tracking

In recent years, demand has been increasing for target detection and tracking from aerial imagery via drones using onboard powered sensors and devices. We propose a very effective method for this application based on a deep learning framework. A state-of-the-art embedded hardware system empowers small flying robots to carry out the real-time onboard computation necessary for object tracking. Two types of embedded modules were developed: one was designed using a Jetson TX or AGX Xavier, and the other was based on an Intel Neural Compute Stick. These are suitable for real-time onboard computing power on small flying drones with limited space. A comparative analysis of current state-of-the-art deep learning-based multi-object detection algorithms was carried out utilizing the designated GPU-based embedded computing modules to obtain detailed metric data about frame rates, as well as the computation power. We also introduce an effective target tracking approach for moving objects. The algorithm for tracking moving objects is based on the extension of simple online and real-time tracking. It was developed by integrating a deep learning-based association metric approach with simple online and real-time tracking (Deep SORT), which uses a hypothesis tracking methodology with Kalman filtering and a deep learning-based association metric. In addition, a guidance system that tracks the target position using a GPU-based algorithm is introduced. Finally, we demonstrate the effectiveness of the proposed algorithms by real-time experiments with a small multi-rotor drone.

Accelerating real‐time object detection in high‐resolution video surveillance

2021 ◽  
Author(s):  
Yuefeng Wang ◽  
Kuang Mao ◽  
Tong Chen ◽  
Yanglong Yin ◽  
Shuibing He ◽  
...  
Keyword(s):  
High Resolution ◽  
Object Detection ◽  
Real Time ◽  
Video Surveillance

CenterTrack3D: Improved CenterTrack more Suitable for 3D Objects

2021 ◽  
pp. 1-32
Author(s):  
Lipeng Gu ◽  
Shaoyuan Sun ◽  
Xunhua Liu ◽  
Xiang Li
Keyword(s):  
Object Tracking ◽  
Research Field ◽  
Unmanned Vehicles ◽  
Current Frame ◽  
3D Objects ◽  
Detection And Tracking ◽  
Tracking Algorithms ◽  
Occluded Objects ◽  
Previous Frame ◽  
3D Information

Abstract Compared with 2D multi-object tracking algorithms, 3D multi-object tracking algorithms have more research significance and broad application prospects in the unmanned vehicles research field. Aiming at the problem of 3D multi-object detection and tracking, in this paper, the multi-object tracker CenterTrack, which focuses on 2D multi-object tracking task while ignoring object 3D information, is improved mainly from two aspects of detection and tracking, and the improved network is called CenterTrack3D. In terms of detection, CenterTrack3D uses the idea of attention mechanism to optimize the way that the previous-frame image and the heatmap of previous-frame tracklets are added to the current-frame image as input, and second convolutional layer of the output head is replaced by dynamic convolution layer, which further improves the ability to detect occluded objects. In terms of tracking, a cascaded data association algorithm based on 3D Kalman filter is proposed to make full use of the 3D information of objects in the image and increase the robustness of the 3D multi-object tracker. The experimental results show that, compared with the original CenterTrack and the existing 3D multi-object tracking methods, CenterTrack3D achieves 88.75% MOTA for cars and 59.40% MOTA for pedestrians and is very competitive on the KITTI tracking benchmark test set.

Sign in / Sign up

Export Citation Format

Share Document