Enhanced the moving object detection and object tracking for traffic surveillance using RBF-FDLNN and CBF algorithm

Chandrakar, Ramakant; Raja, Rohit; Miri, Rohit; Sinha, Upasana; Kushwaha, Alok Kumar Singh; Raja, Hiral

doi:10.1016/j.eswa.2021.116306

Cited by 42 publications

(10 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Foreground extraction algorithms are widely used in camera surveillance to extract foreground targets (moving targets) from background images in video stream data, which is a prerequisite for target tracking and anomaly monitoring [13]. Our worker aberrant behavior detection system is used to identify human targets and gather motion feature data, which is a crucial step in later behavior recognition.…”

Section: Traditional Foreground Extraction Methodsmentioning

confidence: 99%

Worker Abnormal Behavior Recognition Based on Spatio-Temporal Graph Convolution and Attention Model

Zhang

Han

et al. 2023

Electronics

View full text Add to dashboard Cite

In response to the problem where many existing research models only consider acquiring the temporal information between sequences of continuous skeletons and in response to the lack of the ability to model spatial information, this study proposes a model for recognizing worker falls and lays out abnormal behaviors based on human skeletal key points and a spatio-temporal graph convolutional network (ST-GCN). Skeleton extraction of the human body in video sequences was performed using Alphapose. To resolve the problem of graph convolutional networks not being effective enough for skeletal key points feature aggregation, we propose an NAM-STGCN model that incorporates a normalized attention mechanism. By using the activation function PReLU to optimize the model structure, the improved ST-GCN model can more effectively extract skeletal key points action features in the spatio-temporal dimension for the purposes of abnormal behavior recognition. The experimental results show that our optimized model achieves a 96.72% accuracy for recognition on the self-built dataset, which is 4.92% better than the original model; the model loss value converges below 0.2. Tests were performed on the KTH and Le2i datasets, which are both better than typical classification recognition networks. The model can precisely identify abnormal human behaviors, facilitating the detection of abnormalities and rescue in a timely manner and offering novel ideas for smart site construction.

show abstract

Section: Traditional Foreground Extraction Methodsmentioning

confidence: 99%

Worker Abnormal Behavior Recognition Based on Spatio-Temporal Graph Convolution and Attention Model

Zhang

Han

et al. 2023

Electronics

View full text Add to dashboard Cite

show abstract

“…Multi-Object Tracking Datasets. MOT is an essential part of important applications such as autonomous driving [23,46,48], smart city [18,40,12,13] and activity recognition [61,5]. Especially the field of autonomous driving has grown significantly, which is also reflected in the number of large-scale benchmarks published for this scenario [29,16,33,55,9,14,22,64].…”

Section: Related Workmentioning

confidence: 99%

Large Scale Real-World Multi-person Tracking

Shuai

Bergamo

Büchler

et al. 2022

Lecture Notes in Computer Science

View full text Add to dashboard Cite

This paper presents a new large scale multi-person tracking dataset -PersonPath22, which is over an order of magnitude larger than currently available high quality multi-object tracking datasets such as MOT17, HiEve, and MOT20 datasets. The lack of large scale training and test data for this task has limited the community's ability to understand the performance of their tracking systems on a wide range of scenarios and conditions such as variations in person density, actions being performed, weather, and time of day. PersonPath22 dataset was specifically sourced to provide a wide variety of these conditions and our annotations include rich meta-data such that the performance of a tracker can be evaluated along these different dimensions. The lack of training data has also limited the ability to perform end-to-end training of tracking systems. As such, the highest performing tracking systems all rely on strong detectors trained on external image datasets. We hope that the release of this dataset will enable new lines of research that take advantage of large scale video based training data.

show abstract

“…Single object tracking algorithms became popular and gained interest in resent years because of its wide range of applications in computer vision, including video surveillance [3], augmented reality [4], automated driving [5], mobile robotics [6], traffic monitoring [7], sports video analysis [8], scene understanding [9], and human computer interaction [10]. Single object VOT approaches captures the target's appearance features in the first frame of a video sequence and then use it to locate the target in the remaining frames.…”

Section: Introductionmentioning

confidence: 99%

Transformers in Single Object Tracking: An Experimental Survey

Thangavel¹,

Thanikasalam²,

Ramanan³

et al. 2023

Preprint

View full text Add to dashboard Cite

Single object tracking is a well-known and challenging research topic in computer vision. Over the last two decades, numerous researchers have proposed various algorithms to solve this problem and achieved promising results. Recently, Transformer-based tracking approaches have ushered in a new era in single object tracking due to their superior tracking robustness. Although several survey studies have been conducted to analyze the performance of trackers, there is a need for another survey study after the introduction of Transformers in single object tracking. In this survey, we aim to analyze the literature and performances of Transformer tracking approaches. Therefore, we conduct an in-depth literature analysis of Transformer tracking approaches and evaluate their tracking robustness and computational efficiency on challenging benchmark datasets. In addition, we have measured their performances on different tracking scenarios to find their strength and weaknesses. Our survey provides insights into the underlying principles of Transformer tracking approaches, the challenges they face, and their future directions.

show abstract

Enhanced the moving object detection and object tracking for traffic surveillance using RBF-FDLNN and CBF algorithm

Cited by 42 publications

References 26 publications

Worker Abnormal Behavior Recognition Based on Spatio-Temporal Graph Convolution and Attention Model

Worker Abnormal Behavior Recognition Based on Spatio-Temporal Graph Convolution and Attention Model

Large Scale Real-World Multi-person Tracking

Transformers in Single Object Tracking: An Experimental Survey

Contact Info

Product

Resources

About