Multiple Objects Tracking with Multiple Hypotheses Graph Representation

2006 ◽  
Author(s):  
Alex Yong Sang Chia ◽  
Weimin Huang ◽  
Liyuan Li
Keyword(s):  
Graph Representation ◽  
Multiple Objects ◽  
Multiple Hypotheses ◽  
Objects Tracking

scholarly journals Multiple objects tracking in the UAV system based on hierarchical deep high-resolution network

2021 ◽  
Author(s):  
Wei Huang ◽  
Xiaoshu Zhou ◽  
Mingchao Dong ◽  
Huaiyu Xu
Keyword(s):  
High Resolution ◽  
Object Tracking ◽  
High Performance ◽  
State Of The Art ◽  
Class Imbalance ◽  
Unified Framework ◽  
Multiple Objects ◽  
Tracking Process ◽  
Objects Tracking ◽  
Different Types

AbstractRobust and high-performance visual multi-object tracking is a big challenge in computer vision, especially in a drone scenario. In this paper, an online Multi-Object Tracking (MOT) approach in the UAV system is proposed to handle small target detections and class imbalance challenges, which integrates the merits of deep high-resolution representation network and data association method in a unified framework. Specifically, while applying tracking-by-detection architecture to our tracking framework, a Hierarchical Deep High-resolution network (HDHNet) is proposed, which encourages the model to handle different types and scales of targets, and extract more effective and comprehensive features during online learning. After that, the extracted features are fed into different prediction networks for interesting targets recognition. Besides, an adjustable fusion loss function is proposed by combining focal loss and GIoU loss to solve the problems of class imbalance and hard samples. During the tracking process, these detection results are applied to an improved DeepSORT MOT algorithm in each frame, which is available to make full use of the target appearance features to match one by one on a practical basis. The experimental results on the VisDrone2019 MOT benchmark show that the proposed UAV MOT system achieves the highest accuracy and the best robustness compared with state-of-the-art methods.

Re-Identification with Pre-Filtering by Image Quality for Multiple Objects Tracking

2021 ◽  
Vol 27 (8) ◽  
pp. 409-418
Author(s):  
A. D. Grigorev ◽  
◽  
A. N. Gneushev ◽  
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Object Tracking ◽  
Multiple Object Tracking ◽  
Tracking Problem ◽  
Multiple Objects ◽  
Multiple Object ◽  
Objects Tracking ◽  
Error Accumulation ◽  
The Stability

The paper considers multiple object tracking. Existing methods tend to be either resource-intensive or prone to high object densities errors failing to provide competitive performance at high frame rates without significant tracking disruptions and error accumulation. We formulate the multiple object tracking problem under the assumption of linearity and independence of the movement of objects. The factorization of the posterior distribution of objects' parameters provides proof of the equivalence of the initial problem and the tracking procedure containing two subtasks: track prediction and assignment of measurements and objects. A modification of the assignment cost is introduced to achieve the stability of assignments in challenging scenarios of tracking, such as multiple objects occlusions and missing detections. We consider adding a term that states to re-identification of the candidate by comparing its descriptor with descriptors from the track history. Given that track measurements are not equal in terms of usefulness for re-identification, we introduce the technique of track descriptor pre-filtering based on quality assessment in order to select the most relevant descriptors for re-identification and reduce method algorithmic complexity. Both known quality assessment methods and an alternative detector-based approach are taken into account. Computational experiments were conducted on MOT20-01, MOT20-02 datasets containing CCTVcameras data in order to compare the proposed method with other approaches. The results showed the computational efficiency of the proposed methods and the increased stability of tracking in complex scenarios.

scholarly journals Multiple Objects Tracking With Improved Sparse Representation and Rank Based Dynamic Estimation

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 42264-42278 ◽  
Author(s):  
Honghong Yang ◽  
Shiru Qu ◽  
Changsheng Chen ◽  
Bo Yang
Keyword(s):  
Sparse Representation ◽  
Multiple Objects ◽  
Objects Tracking ◽  
Dynamic Estimation

Real time multiple objects tracking based on a bio-inspired processing cascade architecture

2010 ◽  
Author(s):  
F. Gomez-Rodriguez ◽  
L. Miro-Amarante ◽  
F. Diaz-del-Rio ◽  
A. Linares-Barranco ◽  
G. Jimenez. Robotics
Keyword(s):  
Real Time ◽  
Multiple Objects ◽  
Objects Tracking
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