Journal ArticleDOI
On-Road Pedestrian Tracking Across Multiple Driving Recorders
Kuan-Hui Lee,Jenq-Neng Hwang +1 more
TLDR
A new framework to track on-road pedestrians across multiple driving recorders built upon the results of tracking under a single driving recorder to determine whether a specific pedestrian belongs to one or several cameras' field of views by considering association likelihood of the tracked pedestrians.Abstract:
In this paper, we propose a new framework to track on-road pedestrians across multiple driving recorders The framework is built upon the results of tracking under a single driving recorder More specifically, we treat the problem as a multi-label classification task and determine whether a specific pedestrian belongs to one or several cameras’ field of views by considering association likelihood of the tracked pedestrians The likelihood is calculated based on the pedestrians’ motion cues and appearance features, which are necessarily transformed via brightness transfer functions obtained by some available spatially overlapping views for compensating diversity of the cameras When a pedestrian is leaving a camera’s field of view, the proposed framework predicts and interpolates its possible moving trajectories, facilitated by open map service which can provide routing information Experimental results show the robustness and effectiveness of the proposed framework in tracking pedestrians across several recorded driving videos Moreover, based on the GPS locations, we can also reconstruct a 3-D visualization on a 3-D virtual real-world environment, so as to show the dynamic scenes of the recorded videosread more
Citations
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Proceedings ArticleDOI
High Performance Visual Tracking with Siamese Region Proposal Network
TL;DR: The Siamese region proposal network (Siamese-RPN) is proposed which is end-to-end trained off-line with large-scale image pairs for visual object tracking and consists of SiAMESe subnetwork for feature extraction and region proposal subnetwork including the classification branch and regression branch.
Proceedings ArticleDOI
Deformable Siamese Attention Networks for Visual Object Tracking
TL;DR: This paper proposes SiamAttn, a new Siamese attention mechanism that computes deformable self-attention and cross-att attention, capable of aggregating rich contextual interdependencies between the target template and the search image, for more accurate tracking.
Proceedings ArticleDOI
STMTrack: Template-free Visual Tracking with Space-time Memory Networks
TL;DR: Zhang et al. as mentioned in this paper proposed a novel tracking framework built on top of a space-time memory network that is competent to make full use of historical information related to the target for better adapting to appearance variations during tracking.
Posted Content
STMTrack: Template-free Visual Tracking with Space-time Memory Networks
TL;DR: A novel tracking framework built on top of a space-time memory network that is competent to make full use of historical information related to the target for better adapting to appearance variations during tracking is proposed.
Journal ArticleDOI
Pedestrian Detection for Autonomous Vehicle Using Multi-Spectral Cameras
Zhilu Chen,Xinming Huang +1 more
TL;DR: A novel instrument for pedestrian detection by combining stereo vision cameras with a thermal camera is presented, and it significantly outperforms the traditional histogram of oriented gradients features.
References
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Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: This paper presents a method for extracting distinctive invariant features from images that can be used to perform reliable matching between different views of an object or scene and can robustly identify objects among clutter and occlusion while achieving near real-time performance.
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Richard Hartley,Andrew Zisserman +1 more
TL;DR: In this article, the authors provide comprehensive background material and explain how to apply the methods and implement the algorithms directly in a unified framework, including geometric principles and how to represent objects algebraically so they can be computed and applied.
Distinctive Image Features from Scale-Invariant Keypoints
TL;DR: The Scale-Invariant Feature Transform (or SIFT) algorithm is a highly robust method to extract and consequently match distinctive invariant features from images that can then be used to reliably match objects in diering images.
Multiple View Geometry in Computer Vision.
TL;DR: This book is referred to read because it is an inspiring book to give you more chance to get experiences and also thoughts and it will show the best book collections and completed collections.
Journal ArticleDOI
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
TL;DR: A generalized gray-scale and rotation invariant operator presentation that allows for detecting the "uniform" patterns for any quantization of the angular space and for any spatial resolution and presents a method for combining multiple operators for multiresolution analysis.