Multiple Camera Human Detection and Tracking inside a Robotic Cell - An Approach based on Image War, Computer Vision, K-d Trees and Particle Filtering

2014 ◽  
Author(s):  
Matteo Ragaglia ◽  
Luca Bascetta ◽  
Paolo Rocco
Keyword(s):  
Computer Vision ◽  
Particle Filtering ◽  
Human Detection ◽  
Robotic Cell ◽  
Multiple Camera ◽  
Detection And Tracking

scholarly journals An Intelligent Automatic Human Detection and Tracking System Based on Weighted Resampling Particle Filtering

2020 ◽  
Vol 4 (4) ◽  
pp. 27
Author(s):  
Liang Cheng Chang ◽  
Shreya Pare ◽  
Mahendra Singh Meena ◽  
Deepak Jain ◽  
Dong Lin Li ◽  
...  
Keyword(s):  
Particle Filtering ◽  
Tracking System ◽  
Human Detection ◽  
Surveillance Systems ◽  
Camera Control ◽  
Detection And Tracking ◽  
Filter Approach ◽  
And Performance ◽  
The Stability ◽  
Active Camera

At present, traditional visual-based surveillance systems are becoming impractical, inefficient, and time-consuming. Automation-based surveillance systems appeared to overcome these limitations. However, the automatic systems have some challenges such as occlusion and retaining images smoothly and continuously. This research proposes a weighted resampling particle filter approach for human tracking to handle these challenges. The primary functions of the proposed system are human detection, human monitoring, and camera control. We used the codebook matching algorithm to define the human region as a target and track it, and we used the practical filter algorithm to follow and extract the target information. Consequently, the obtained information was used to configure the camera control. The experiments were tested in various environments to prove the stability and performance of the proposed system based on the active camera.

Multi Object Detection and Tracking from Video File

2014 ◽  
Vol 533 ◽  
pp. 218-225 ◽  
Author(s):  
Rapee Krerngkamjornkit ◽  
Milan Simic
Keyword(s):  
Computer Vision ◽  
Background Subtraction ◽  
Moving Objects ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
Video Tracking ◽  
Partial Occlusion ◽  
Video File ◽  
Detection And Tracking ◽  
Over Time

This paper describes computer vision algorithms for detection, identification, and tracking of moving objects in a video file. The problem of multiple object tracking can be divided into two parts; detecting moving objects in each frame and associating the detections corresponding to the same object over time. The detection of moving objects uses a background subtraction algorithm based on Gaussian mixture models. The motion of each track is estimated by a Kalman filter. The video tracking algorithm was successfully tested using the BIWI walking pedestrians datasets [. The experimental results show that system can operate in real time and successfully detect, track and identify multiple targets in the presence of partial occlusion.

Multi-Robot Multiple Camera People Detection and Tracking in Automated Warehouses

2021 ◽  
Author(s):  
Michela Zaccaria ◽  
Mikhail Giorgini ◽  
Riccardo Monica ◽  
Jacopo Aleotti
Keyword(s):  
People Detection ◽  
Multiple Camera ◽  
Detection And Tracking ◽  
Automated Warehouses ◽  
Multi Robot

scholarly journals Privacy-Aware Human-Detection and Tracking System Using Biological Signals

2019 ◽  
Vol E102.B (4) ◽  
pp. 708-721
Author(s):  
Toshihiro KITAJIMA ◽  
Edwardo Arata Y. MURAKAMI ◽  
Shunsuke YOSHIMOTO ◽  
Yoshihiro KURODA ◽  
Osamu OSHIRO
Keyword(s):  
Tracking System ◽  
Human Detection ◽  
Detection And Tracking ◽  
Biological Signals

scholarly journals Convolutional Neural Network Using Kalman Filter for Human Detection and Tracking on RGB-D Video

2018 ◽  
Vol 12 (2) ◽  
pp. 105 ◽  
Author(s):  
Jovin Angelico ◽  
Ken Ratri Retno Wardani
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Convolutional Neural Network ◽  
Human Detection ◽  
Detection Accuracy ◽  
Tracking Accuracy ◽  
Lighting Condition ◽  
Detection And Tracking ◽  
Computation Efficiency ◽  
The Impact

The computer ability to detect human being by computer vision is still being improved both in accuracy or computation time. In low-lighting condition, the detection accuracy is usually low. This research uses additional information, besides RGB channels, namely a depth map that shows objects’ distance relative to the camera. This research integrates Cascade Classifier (CC) to localize the potential object, the Convolutional Neural Network (CNN) technique to identify the human and nonhuman image, and the Kalman filter technique to track human movement. For training and testing purposes, there are two kinds of RGB-D datasets used with different points of view and lighting conditions. Both datasets have been selected to remove images which contain a lot of noises and occlusions so that during the training process it will be more directed. Using these integrated techniques, detection and tracking accuracy reach 77.7%. The impact of using Kalman filter increases computation efficiency by 41%.

Visual Attention Guided Object Detection and Tracking

2016 ◽  
pp. 99-114
Author(s):  
Debi Prosad Dogra
Keyword(s):  
Computer Vision ◽  
Object Recognition ◽  
Visual Attention ◽  
Visual Saliency ◽  
Video Object ◽  
Salient Region Detection ◽  
Region Detection ◽  
Video Object Tracking ◽  
Detection And Tracking ◽  
Pros And Cons

Scene understanding and object recognition heavily depend on the success of visual attention guided salient region detection in images and videos. Therefore, summarizing computer vision techniques that take the help of visual attention models to accomplish video object recognition and tracking, can be helpful to the researchers of computer vision community. In this chapter, it is aimed to present a philosophical overview of the possible applications of visual attention models in the context of object recognition and tracking. At the beginning of this chapter, a brief introduction to various visual saliency models suitable for object recognition is presented, that is followed by discussions on possible applications of attention models on video object tracking. The chapter also provides a commentary on the existing techniques available on this domain and discusses some of their possible extensions. It is believed that, prospective readers will benefit since the chapter comprehensively guides a reader to understand the pros and cons of this particular topic.

Visual Tracking Using Multimodal Particle Filter

2018 ◽  
pp. 1072-1090 ◽  
Author(s):  
Tony Tung ◽  
Takashi Matsuyama
Keyword(s):  
Visual Tracking ◽  
Particle Filtering ◽  
Body Parts ◽  
Detection And Tracking ◽  
Flow Features ◽  
Target Templates ◽  
Textured Models ◽  
Online Mechanism ◽  
Changes Over Time ◽  
Appearance Changes

Visual tracking of humans or objects in motion is a challenging problem when observed data undergo appearance changes (e.g., due to illumination variations, occlusion, cluttered background, etc.). Moreover, tracking systems are usually initialized with predefined target templates, or trained beforehand using known datasets. Hence, they are not always efficient to detect and track objects whose appearance changes over time. In this paper, we propose a multimodal framework based on particle filtering for visual tracking of objects under challenging conditions (e.g., tracking various human body parts from multiple views). Particularly, the authors integrate various cues such as color, motion and depth in a global formulation. The Earth Mover distance is used to compare color models in a global fashion, and constraints on motion flow features prevent common drifting effects due to error propagation. In addition, the model features an online mechanism that adaptively updates a subspace of multimodal templates to cope with appearance changes. Furthermore, the proposed model is integrated in a practical detection and tracking process, and multiple instances can run in real-time. Experimental results are obtained on challenging real-world videos with poorly textured models and arbitrary non-linear motions.

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