Suya Li¹, Ying Cao^1,*, Hengyi Ren², Dongsheng Zhu³, Xin Xie¹

CMC-Computers, Materials & Continua, Vol.81, No.1, pp. 1449-1470, 2024, DOI:10.32604/cmc.2024.056824 - 15 October 2024

Abstract Multi-object tracking (MOT) has seen rapid improvements in recent years. However, frequent occlusion remains a significant challenge in MOT, as it can cause targets to become smaller or disappear entirely, resulting in low-quality targets, leading to trajectory interruptions and reduced tracking performance. Different from some existing methods, which discarded the low-quality targets or ignored low-quality target attributes. LQTTrack, with a low-quality association strategy (LQA), is proposed to pay more attention to low-quality targets. In the association scheme of LQTTrack, firstly, multi-scale feature fusion of FPN (MSFF-FPN) is utilized to enrich the feature information and assist… More >

Chunjuan Bo^1,*, Xin Chen², Junxing Zhang¹

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 1105-1122, 2024, DOI:10.32604/cmc.2024.052329 - 18 July 2024

Abstract Significant advancements have been witnessed in visual tracking applications leveraging ViT in recent years, mainly due to the formidable modeling capabilities of Vision Transformer (ViT). However, the strong performance of such trackers heavily relies on ViT models pretrained for long periods, limiting more flexible model designs for tracking tasks. To address this issue, we propose an efficient unsupervised ViT pretraining method for the tracking task based on masked autoencoders, called TrackMAE. During pretraining, we employ two shared-parameter ViTs, serving as the appearance encoder and motion encoder, respectively. The appearance encoder encodes randomly masked image data,… More >

Zhongyang Wang, Hu Zhu, Feng Liu^*

CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 605-623, 2024, DOI:10.32604/cmc.2024.050959 - 18 July 2024

Abstract Visual object tracking plays a crucial role in computer vision. In recent years, researchers have proposed various methods to achieve high-performance object tracking. Among these, methods based on Transformers have become a research hotspot due to their ability to globally model and contextualize information. However, current Transformer-based object tracking methods still face challenges such as low tracking accuracy and the presence of redundant feature information. In this paper, we introduce self-calibration multi-head self-attention Transformer (SMSTracker) as a solution to these challenges. It employs a hybrid tensor decomposition self-organizing multi-head self-attention transformer mechanism, which not only… More >

Asifa Mehmood Qureshi¹, Naif Al Mudawi², Mohammed Alonazi³, Samia Allaoua Chelloug⁴, Jeongmin Park^5,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3683-3701, 2024, DOI:10.32604/cmc.2024.043611 - 26 March 2024

Abstract Road traffic monitoring is an imperative topic widely discussed among researchers. Systems used to monitor traffic frequently rely on cameras mounted on bridges or roadsides. However, aerial images provide the flexibility to use mobile platforms to detect the location and motion of the vehicle over a larger area. To this end, different models have shown the ability to recognize and track vehicles. However, these methods are not mature enough to produce accurate results in complex road scenes. Therefore, this paper presents an algorithm that combines state-of-the-art techniques for identifying and tracking vehicles in conjunction with… More >

Ye-Yeon Kang¹, Geon Park¹, Hyun Yoo², Kyungyong Chung^1,*

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3619-3635, 2023, DOI:10.32604/cmc.2023.043566 - 26 December 2023

Abstract Object tracking, an important technology in the field of image processing and computer vision, is used to continuously track a specific object or person in an image. This technology may be effective in identifying the same person within one image, but it has limitations in handling multiple images owing to the difficulty in identifying whether the object appearing in other images is the same. When tracking the same object using two or more images, there must be a way to determine that objects existing in different images are the same object. Therefore, this paper attempts… More >

Zhenyu Huang^1,2, Gun Li², Xudong Sun¹, Yong Chen¹, Jie Sun¹, Zhangsong Ni^1,*, Yang Yang^1,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3219-3238, 2023, DOI:10.32604/cmc.2023.039489 - 08 October 2023

Abstract Onboard visual object tracking in unmanned aerial vehicles (UAVs) has attracted much interest due to its versatility. Meanwhile, due to high precision, Siamese networks are becoming hot spots in visual object tracking. However, most Siamese trackers fail to balance the tracking accuracy and time within onboard limited computational resources of UAVs. To meet the tracking precision and real-time requirements, this paper proposes a Siamese dense pixel-level network for UAV object tracking named SiamDPL. Specifically, the Siamese network extracts features of the search region and the template region through a parameter-shared backbone network, then performs correlation… More >

Shuja Ali¹, Ahmad Jalal¹, Mohammed Hamad Alatiyyah², Khaled Alnowaiser³, Jeongmin Park^4,*

CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 1249-1265, 2023, DOI:10.32604/cmc.2023.038114 - 08 June 2023

Abstract Unmanned aerial vehicles (UAVs) can be used to monitor traffic in a variety of settings, including security, traffic surveillance, and traffic control. Numerous academics have been drawn to this topic because of the challenges and the large variety of applications. This paper proposes a new and efficient vehicle detection and tracking system that is based on road extraction and identifying objects on it. It is inspired by existing detection systems that comprise stationary data collectors such as induction loops and stationary cameras that have a limited field of view and are not mobile. The goal… More >

Jinping Sun^*, Dan Li, Honglin Cheng

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 655-675, 2023, DOI:10.32604/iasc.2023.038016 - 29 April 2023

Abstract Aiming at the problem that a single correlation filter model is sensitive to complex scenes such as background interference and occlusion, a tracking algorithm based on multi-time-space perception and instancespecific proposals is proposed to optimize the mathematical model of the correlation filter (CF). Firstly, according to the consistency of the changes between the object frames and the filter frames, the mask matrix is introduced into the objective function of the filter, so as to extract the spatio-temporal information of the object with background awareness. Secondly, the object function of multi-feature fusion is constructed for the… More >

Yannan Cai, Ke Tan, Zhenzhong Wei^*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1621-1640, 2023, DOI:10.32604/cmc.2023.036177 - 06 February 2023

Abstract Label assignment refers to determining positive/negative labels for each sample to supervise the training process. Existing Siamese-based trackers primarily use fixed label assignment strategies according to human prior knowledge; thus, they can be sensitive to predefined hyperparameters and fail to fit the spatial and scale variations of samples. In this study, we first develop a novel dynamic label assignment (DLA) module to handle the diverse data distributions and adaptively distinguish the foreground from the background based on the statistical characteristics of the target in visual object tracking. The core of DLA module is a two-step… More >

Aparna Gullapelly, Barnali Gupta Banik^*

Computer Systems Science and Engineering, Vol.46, No.1, pp. 1127-1139, 2023, DOI:10.32604/csse.2023.032904 - 20 January 2023

Abstract The field of object tracking has recently made significant progress. Particularly, the performance results in both deep learning and correlation filters, based trackers achieved effective tracking performance. Moreover, there are still some difficulties with object tracking for example illumination and deformation (DEF). The precision and accuracy of tracking algorithms suffer from the effects of such occurrences. For this situation, finding a solution is important. This research proposes a new tracking algorithm to handle this problem. The features are extracted by using Modified LeNet-5, and the precision and accuracy are improved by developing the Real-Time Cross-modality… More >