A scene interpretation approach to high-level target tracking

1990 ◽  
Author(s):  
Scott W. Shaw
Keyword(s):  
Target Tracking ◽  
Scene Interpretation ◽  
High Level

scholarly journals Mask Sparse Representation Based on Semantic Features for Thermal Infrared Target Tracking

2019 ◽  
Vol 11 (17) ◽  
pp. 1967 ◽  
Author(s):  
Meihui Li ◽  
Lingbing Peng ◽  
Yingpin Chen ◽  
Suqi Huang ◽  
Feiyi Qin ◽  
...  
Keyword(s):  
Sparse Representation ◽  
Target Tracking ◽  
Sparse Coding ◽  
Thermal Infrared ◽  
Selection Strategy ◽  
Target Area ◽  
Semantic Features ◽  
Reconstruction Process ◽  
Deep Networks ◽  
High Level

Thermal infrared (TIR) target tracking is a challenging task as it entails learning an effective model to identify the target in the situation of poor target visibility and clutter background. The sparse representation, as a typical appearance modeling approach, has been successfully exploited in the TIR target tracking. However, the discriminative information of the target and its surrounding background is usually neglected in the sparse coding process. To address this issue, we propose a mask sparse representation (MaskSR) model, which combines sparse coding together with high-level semantic features for TIR target tracking. We first obtain the pixel-wise labeling results of the target and its surrounding background in the last frame, and then use such results to train target-specific deep networks using a supervised manner. According to the output features of the deep networks, the high-level pixel-wise discriminative map of the target area is obtained. We introduce the binarized discriminative map as a mask template to the sparse representation and develop a novel algorithm to collaboratively represent the reliable target part and unreliable target part partitioned with the mask template, which explicitly indicates different discriminant capabilities by label 1 and 0. The proposed MaskSR model controls the superiority of the reliable target part in the reconstruction process via a weighted scheme. We solve this multi-parameter constrained problem by a customized alternating direction method of multipliers (ADMM) method. This model is applied to achieve TIR target tracking in the particle filter framework. To improve the sampling effectiveness and decrease the computation cost at the same time, a discriminative particle selection strategy based on kernelized correlation filter is proposed to replace the previous random sampling for searching useful candidates. Our proposed tracking method was tested on the VOT-TIR2016 benchmark. The experiment results show that the proposed method has a significant superiority compared with various state-of-the-art methods in TIR target tracking.

scholarly journals Persistent Multi-Agent Search and Tracking with Flight Endurance Constraints

Robotics ◽  
2018 ◽  
Vol 8 (1) ◽  
pp. 2 ◽  
Author(s):  
John Gainer Jr. ◽  
Jeremy Dawkins ◽  
Levi DeVries ◽  
Michael Kutzer
Keyword(s):  
Target Tracking ◽  
Autonomous Vehicles ◽  
Base Station ◽  
Search Performance ◽  
Battery Capacity ◽  
Coordination Algorithms ◽  
Multi Agent ◽  
High Level ◽  
Long Endurance ◽  
The Relationship

A large group of small, limited endurance autonomous vehicles working cooperatively may be more effective in target search and track operations when compared with a long endurance vehicle. For a persistent search and track task, a need exists for coordination algorithms that account for limited agent endurance. This paper presents a multi-agent persistent search and track algorithm incorporating endurance constraints in a high-level algorithm that deploys and recovers vehicles from a stationary base station. Agents are assigned to search, track, return, and deploy modes using on-board sensor and battery measurements. Simulations and experiments show the relationship between the number of agents, battery capacity, search performance, and target tracking performance. The measures used to quantify these relationships include spatiotemporal coverage, target tracking effectiveness, and the usage of available aircraft. Hardware experiments demonstrate the effectiveness of the approach.

Research on Mean Shift Algorithm

2013 ◽  
Vol 756-759 ◽  
pp. 4021-4025 ◽  
Author(s):  
Yi Zhi Zhao ◽  
Huan Wang ◽  
Guo Cai Yin
Keyword(s):  
Computer Vision ◽  
Object Tracking ◽  
Target Tracking ◽  
Mean Shift ◽  
Moving Target ◽  
Object Model ◽  
Probability Density Estimation ◽  
Complete Occlusion ◽  
Mean Shift Algorithm ◽  
High Level

Computer vision is a diverse and relatively new field of study. Object tracking plays a crucial role as a preliminary step for high-level image processing in the field of computer vision. However, mean shift algorithm in the target tracking has some defects, such as: the application of fixed bandwidth for probability density estimation usually causes lack of smooth or too smooth; moving target often appears partial occlusion or complete occlusion due to the complexity of the background; background pixels in object model will induce localization error of object tracking, and so on. Therefore, this paper elaborates several elegant algorithms to solve some of the problems. After discussing the application of Mean shift in the field of target tracking, this paper presented an improved Mean shift algorithm by combining Mean Shift and Kalman Filter.

Transcription factor/DNA interactions visualized by electron spectroscopic imaging

1992 ◽  
Vol 50 (1) ◽  
pp. 450-451
Author(s):  
David P. Bazett-Jones ◽  
Mark L. Brown
Keyword(s):  
Transcription Factor ◽  
Dna Sequences ◽  
Transcription Initiation ◽  
Molecular Mechanisms ◽  
Phosphorus Content ◽  
Spectroscopic Imaging ◽  
Electron Spectroscopic Imaging ◽  
Dna Backbone ◽  
Two Parameters ◽  
High Level

A multisubunit RNA polymerase enzyme is ultimately responsible for transcription initiation and elongation of RNA, but recognition of the proper start site by the enzyme is regulated by general, temporal and gene-specific trans-factors interacting at promoter and enhancer DNA sequences. To understand the molecular mechanisms which precisely regulate the transcription initiation event, it is crucial to elucidate the structure of the transcription factor/DNA complexes involved. Electron spectroscopic imaging (ESI) provides the opportunity to visualize individual DNA molecules. Enhancement of DNA contrast with ESI is accomplished by imaging with electrons that have interacted with inner shell electrons of phosphorus in the DNA backbone. Phosphorus detection at this intermediately high level of resolution (≈lnm) permits selective imaging of the DNA, to determine whether the protein factors compact, bend or wrap the DNA. Simultaneously, mass analysis and phosphorus content can be measured quantitatively, using adjacent DNA or tobacco mosaic virus (TMV) as mass and phosphorus standards. These two parameters provide stoichiometric information relating the ratios of protein:DNA content.

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