scholarly journals Object Tracking in Vary Lighting Conditions for Fog Based Intelligent Surveillance of Public Spaces

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 29283-29296 ◽  
Author(s):  
Gaocheng Liu ◽  
Shuai Liu ◽  
Khan Muhammad ◽  
Arun Kumar Sangaiah ◽  
Faiyaz Doctor
Keyword(s):  
Object Tracking ◽  
Public Spaces ◽  
Intelligent Surveillance ◽  
Lighting Conditions

scholarly journals Pose-Assisted Multi-Camera Collaboration for Active Object Tracking

2020 ◽  
Vol 34 (01) ◽  
pp. 759-766
Author(s):  
Jing Li ◽  
Jing Xu ◽  
Fangwei Zhong ◽  
Xiangyu Kong ◽  
Yu Qiao ◽  
...  
Keyword(s):  
Mobile Robot ◽  
Object Tracking ◽  
Experimental Results ◽  
The Other ◽  
Single Camera ◽  
Active Object ◽  
Intelligent Surveillance ◽  
Effective Collaboration

Active Object Tracking (AOT) is crucial to many vision-based applications, e.g., mobile robot, intelligent surveillance. However, there are a number of challenges when deploying active tracking in complex scenarios, e.g., target is frequently occluded by obstacles. In this paper, we extend the single-camera AOT to a multi-camera setting, where cameras tracking a target in a collaborative fashion. To achieve effective collaboration among cameras, we propose a novel Pose-Assisted Multi-Camera Collaboration System, which enables a camera to cooperate with the others by sharing camera poses for active object tracking. In the system, each camera is equipped with two controllers and a switcher: The vision-based controller tracks targets based on observed images. The pose-based controller moves the camera in accordance to the poses of the other cameras. At each step, the switcher decides which action to take from the two controllers according to the visibility of the target. The experimental results demonstrate that our system outperforms all the baselines and is capable of generalizing to unseen environments. The code and demo videos are available on our website https://sites.google.com/view/pose-assisted-collaboration.

scholarly journals Enhanced Median Flow Tracker Based on Photometric Correction for Videos with Abrupt Changing Illumination

2019 ◽  
Vol 17 (2) ◽  
pp. 264-271
Author(s):  
Asha Narayana ◽  
Narasimhadhan Venkata
Keyword(s):  
Object Tracking ◽  
Low Frequency ◽  
Fixed Number ◽  
Video Frame ◽  
Visual Object ◽  
Illumination Variation ◽  
Optical Flow Method ◽  
Lighting Conditions ◽  
Dct Coefficients ◽  
Dc Coefficient

Object tracking is a fundamental task in video surveillance, human-computer interaction and activity analysis. One of the common challenges in visual object tracking is illumination variation. A large number of methods for tracking have been proposed over the recent years, and median flow tracker is one of them which can handle various challenges. Median flow tracker is designed to track an object using Lucas-Kanade optical flow method which is sensitive to illumination variation, hence fails when sudden illumination changes occur between the frames. In this paper, we propose an enhanced median flow tracker to achieve an illumination invariance to abruptly varying lighting conditions. In this approach, illumination variation is compensated by modifying the Discrete Cosine Transform (DCT) coefficients of an image in the logarithmic domain. The illumination variations are mainly reflected in the low-frequency coefficients of an image. Therefore, a fixed number of DCT coefficients are ignored. Moreover, the Discrete Cosine (DC) coefficient is maintained almost constant all through the video based on entropy difference to minimize the sudden variations of lighting impacts. In addition, each video frame is enhanced by employing pixel transformation technique that improves the contrast of dull images based on probability distribution of pixels. The proposed scheme can effectively handle the gradual and abrupt changes in the illumination of the object. The experiments are conducted on fast-changing illumination videos, and results show that the proposed method improves median flow tracker with outperforming accuracy compared to the state-of-the-art trackers

scholarly journals Tracktor: image-based automated tracking of animal movement and behaviour

2018 ◽  
Author(s):  
Vivek Hari Sridhar ◽  
Dominique G. Roche ◽  
Simon Gingins
Keyword(s):  
Object Tracking ◽  
Animal Movement ◽  
Summary Statistics ◽  
Noisy Environments ◽  
Activity Time ◽  
Automated Tracking ◽  
Lighting Conditions ◽  
Cumulative Distance ◽  
Quantitative Analyses ◽  
Movement Tracking

Abstract1. Automated movement tracking is essential for high-throughput quantitative analyses of the behaviour and kinematics of organisms. Automated tracking also improves replicability by avoiding observer biases and allowing reproducible workflows. However, few automated tracking programs exist that are open access, open source, and capable of tracking unmarked organisms in noisy environments.2. Tracktor is an image-based tracking freeware designed to perform single-object tracking in noisy environments, or multi-object tracking in uniform environments while maintaining individual identities. Tracktor is code-based but requires no coding skills other than the user being able to specify tracking parameters in a designated location, much like in a graphical user interface (GUI). The installation and use of the software is fully detailed in a user manual.3. Through four examples of common tracking problems, we show that Tracktor is able to track a variety of animals in diverse conditions. The main strengths of Tracktor lie in its ability to track single individuals under noisy conditions (e.g. when the object shape is distorted), its robustness to perturbations (e.g. changes in lighting conditions during the experiment), and its capacity to track multiple individuals while maintaining their identities. Additionally, summary statistics and plots allow measuring and visualizing common metrics used in the analysis of animal movement (e.g. cumulative distance, speed, acceleration, activity, time spent in specific areas, distance to neighbour, etc.).4. Tracktor is a versatile, reliable, easy-to-use automated tracking software that is compatible with all operating systems and provides many features not available in other existing freeware. Access Tracktor and the complete user manual here: https://github.com/vivekhsridhar/tracktor

Impressions of Lighting in Public Squares After Dark

2016 ◽  
Vol 49 (3) ◽  
pp. 227-254 ◽  
Author(s):  
Jack L. Nasar ◽  
Saleheh Bokharaei
Keyword(s):  
Public Spaces ◽  
Lighting Conditions ◽  
Interior Lighting

Lighting may affect impressions of public squares. Following studies on office interior lighting, the present research manipulated three modes of lighting—non-uniform–uniform, peripheral–overhead, and dim–bright—in three virtual squares. One study had 32 participants (15 men, 17 women) judge the spaciousness and privacy of each of the 24 public squares. A second study had a different group of 30 participants (16 men, 14 women) rate the appeal, safety from crime, and excitement of each square. Study 1 found that judged spaciousness increased with uniform and bright lighting, and that privacy increased with non-uniform, dim, and peripheral lighting. Study 2 found that rated appeal increased with uniform and bright lighting, as did safety from crime and excitement. Across the two studies, the uniform and bright lighting conditions contributed most to the kinds of favorable experiences people might expect to have in public spaces after dark.

Multiple-Object Tracking

2011 ◽  
Vol 58 (3) ◽  
pp. 196-200 ◽  
Author(s):  
K. Botterill ◽  
R. Allen ◽  
P. McGeorge
Keyword(s):  
Object Tracking ◽  
Cognitive Processes ◽  
Spatial Location ◽  
Multiple Object Tracking ◽  
Tracking Performance ◽  
Multiple Object

The Multiple-Object Tracking paradigm has most commonly been utilized to investigate how subsets of targets can be tracked from among a set of identical objects. Recently, this research has been extended to examine the function of featural information when tracking is of objects that can be individuated. We report on a study whose findings suggest that, while participants can only hold featural information for roughly two targets this task does not affect tracking performance detrimentally and points to a discontinuity between the cognitive processes that subserve spatial location and featural information.

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