Multi-object Tracking for Urban and Multilane Traffic: Building Blocks for Real-World Application

Bumanis, Nikolajs; Vītols, Gatis; Arhipova, Irina; Solmanis, Egons

doi:10.5220/0010467807290736

Cited by 6 publications

(4 citation statements)

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“…The algorithm has been compared with a target tracking algorithm based on Convolution Neural Network (TTACNN) [ 44 ], ADT: object tracking algorithm. Based on adaptive detection [ 45 ], vehicle tracking algorithm combining detector and tracker (VTACDT) [ 46 ], multi-object tracking for urban and multilane traffic (MTUMT) [ 47 ], adaptive weighted strategy and occlusion detection mechanism (AWSODM) [ 48 ] and approximate proximal gradient-based correlation filter (APGCF) [ 13 ]. Table 1 shows the execution times of these algorithms, while Table 2 represents the average tracking errors.…”

Section: Resultsmentioning

confidence: 99%

Tracking of a Fixed-Shape Moving Object Based on the Gradient Descent Method

Masood

Zafar

Ali

et al. 2022

Sensors

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Tracking moving objects is one of the most promising yet the most challenging research areas pertaining to computer vision, pattern recognition and image processing. The challenges associated with object tracking range from problems pertaining to camera axis orientations to object occlusion. In addition, variations in remote scene environments add to the difficulties related to object tracking. All the mentioned challenges and problems pertaining to object tracking make the procedure computationally complex and time-consuming. In this paper, a stochastic gradient-based optimization technique has been used in conjunction with particle filters for object tracking. First, the object that needs to be tracked is detected using the Maximum Average Correlation Height (MACH) filter. The object of interest is detected based on the presence of a correlation peak and average similarity measure. The results of object detection are fed to the tracking routine. The gradient descent technique is employed for object tracking and is used to optimize the particle filters. The gradient descent technique allows particles to converge quickly, allowing less time for the object to be tracked. The results of the proposed algorithm are compared with similar state-of-the-art tracking algorithms on five datasets that include both artificial moving objects and humans to show that the gradient-based tracking algorithm provides better results, both in terms of accuracy and speed.

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Section: Resultsmentioning

confidence: 99%

Tracking of a Fixed-Shape Moving Object Based on the Gradient Descent Method

Masood

Zafar

Ali

et al. 2022

Sensors

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“…Nikolajs at el. [28] identified the recent trends of Multi target tracking and determined the necessary computer vision building blocks for application in road traffic scenarios, including surveillance and planning. Ullah at el.…”

Section: Related Workmentioning

confidence: 99%

Multi-target tracking for video surveillance using deep affinity network: A brief review

Mangi¹

2022

Preprint

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Deep learning models are known to function like the human brain. Due to their functional mechanism, they are frequently utilized to accomplish tasks that require human intelligence. Multi-target tracking (MTT) for video surveillance is one of the important and challenging tasks, which has attracted the researchers' attention due to its potential applications in various domains. Multi-target tracking tasks require locating the objects individually in each frame, which remains a huge challenge as there are immediate changes in appearances and extreme occlusions of objects. In addition to that, the Multi-target tracking framework requires multiple tasks to perform i.e. target detection, estimating trajectory, associations between frame, and re-identification. Various methods have been suggested, and some assumptions are made to constrain the problem in the context of a particular problem. In this paper, the state-of-the-art MTT models, which leverage from deep learning representational power are reviewed.

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“…Multiple object tracking (MOT) is one of the most important problem in computer vision and has applications in areas of autonomous robotics [20,50], autonomous driving [12,24,43,52] and smart cities [8,44,52,72]. The problem consists of determining the position and identity of each object of interest (e.g.…”

Section: Introductionmentioning

confidence: 99%

BoostTrack: boosting the similarity measure and detection confidence for improved multiple object tracking

Stanojevic,

Todorovic

2024

Machine Vision and Applications

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Handling unreliable detections and avoiding identity switches are crucial for the success of multiple object tracking (MOT). Ideally, MOT algorithm should use true positive detections only, work in real-time and produce no identity switches. To approach the described ideal solution, we present the BoostTrack, a simple yet effective tracing-by-detection MOT method that utilizes several lightweight plug and play additions to improve MOT performance. We design a detection-tracklet confidence score and use it to scale the similarity measure and implicitly favour high detection confidence and high tracklet confidence pairs in one-stage association. To reduce the ambiguity arising from using intersection over union (IoU), we propose a novel Mahalanobis distance and shape similarity additions to boost the overall similarity measure. To utilize low-detection score bounding boxes in one-stage association, we propose to boost the confidence scores of two groups of detections: the detections we assume to correspond to the existing tracked object, and the detections we assume to correspond to a previously undetected object. The proposed additions are orthogonal to the existing approaches, and we combine them with interpolation and camera motion compensation to achieve results comparable to the standard benchmark solutions while retaining real-time execution speed. When combined with appearance similarity, our method outperforms all standard benchmark solutions on MOT17 and MOT20 datasets. It ranks first among online methods in HOTA metric in the MOT Challenge on MOT17 and MOT20 test sets. We make our code available at https://github.com/vukasin-stanojevic/BoostTrack.

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Multi-object Tracking for Urban and Multilane Traffic: Building Blocks for Real-World Application

Cited by 6 publications

References 0 publications

Tracking of a Fixed-Shape Moving Object Based on the Gradient Descent Method

Tracking of a Fixed-Shape Moving Object Based on the Gradient Descent Method

Multi-target tracking for video surveillance using deep affinity network: A brief review

BoostTrack: boosting the similarity measure and detection confidence for improved multiple object tracking

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