Detection and Tracking of Motorcycles in Congested Urban Environments Using Deep Learning and Markov Decision Processes

Espinosa, Jorge E.; Velastín, Sergio A.; Branch, John W.

doi:10.1007/978-3-030-21077-9_13

Cited by 5 publications

(4 citation statements)

References 17 publications

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“…The low frame rate per second (fps) of Faster R-CNN makes real-time application challenging. In [19], a fourlayer CNN feature extractor serves as the backbone for Faster R-CNN, augmented with the Markov Decision Process (MDP) for tracking on the MB1000 dataset, achieving 88% mean average precision (mAP) in occluded scenarios. However, the compute-intensive two-step detection process results in high inference time, making it impractical for real-time deploy-ment, especially on edge devices.…”

Section: Related Workmentioning

confidence: 99%

“…To encapsulate the diverse approaches employed by various authors in motorbike detection, it is evident that different methods and models have been utilized. Among these, the approach outlined by the authors of [19] stands out prominently, boasting an exceptionally high mean average precision (96%). This work suggests that SSD Mobilenet emerges as the optimal choice for motorbike detection among available methods.…”

Section: Related Workmentioning

confidence: 99%

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Real-Time Motorbike Detection: AI on the Edge Perspective

Akhtar,

Ahmed,

Yousaf

et al. 2024

Mathematics

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Motorbikes are an integral part of transportation in emerging countries, but unfortunately, motorbike users are also one the most vulnerable road users (VRUs) and are engaged in a large number of yearly accidents. So, motorbike detection is very important for proper traffic surveillance, road safety, and security. Most of the work related to bike detection has been carried out to improve accuracy. If this task is not performed in real-time then it loses practical significance, but little to none has been reported for its real-time implementation. In this work, we have looked at multiple real-time deployable cost-efficient solutions for motorbike detection using various state-of-the-art embedded edge devices. This paper discusses an investigation of a proposed methodology on five different embedded devices that include Jetson Nano, Jetson TX2, Jetson Xavier, Intel Compute Stick, and Coral Dev Board. Running the highly compute-intensive object detection model on edge devices (in real-time) is made possible by optimization. As a result, we have achieved inference rates on different devices that are twice as high as GPUs, with only a marginal drop in accuracy. Secondly, the baseline accuracy of motorbike detection has been improved by developing a custom network based on YoloV5 by introducing sparsity and depth reduction. Dataset augmentation has been applied at both image and object levels to enhance robustness of detection. We have achieved 99% accuracy as compared to the previously reported 97% accuracy, with better FPS. Additionally, we have provided a performance comparison of motorbike detection on the different embedded edge devices, for practical implementation.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Real-Time Motorbike Detection: AI on the Edge Perspective

Akhtar,

Ahmed,

Yousaf

et al. 2024

Mathematics

Self Cite

View full text Add to dashboard Cite

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“…These include the registration, tracking, deletion and appearance stages. The following describes the process for the association of identities that allows to reestablish a missing subject or assign a track to an object that is in the registration stage [10,11].…”

Section: Markov Decision Processmentioning

confidence: 99%

Multiple Object Tracking for Robust Quantitative Analysis of Passenger Motion While Boarding and Alighting a Metropolitan Train

Meza

Delpiano²,

Velastín

et al. 2021

IET Conf. Proc.

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To achieve significant improvements in public transport it is necessary to develop an autonomous system that locates and counts passengers in real time in scenarios with a high level of occlusion, providing tools to efficiently solve problems such as reduction and stabilization in travel times, greater fluency, better control of fleets and less congestion. A deep learning method based in transfer learning is used to accomplish this: You Only Look Once (YOLO) version 3 and Faster R-CNN Inception version 2 architectures are fine tuned using PAMELA-UANDES dataset, which contains annotated images of the boarding and alighting of passengers on a subway platform from a superior perspective. The locations given by the detector are passed through a multiple object tracking system implemented based on a Markov decision process that associates subjects in consecutive frames and assigns identities considering overlaps between past detections and predicted positions using a Kalman filter.

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“…Most of them rely on the deep learning tool of convolutional neural network (CNN) to classify the objects, such as to achieve remarkable detection performance. This technological advancement opens new horizons for researchers, owing to its superior performance in many practical disciplines, including object detection [1], object localization [2], object tracking [3], image generation [4], human pose estimation [5], text recognition and detection [6], visual question answering [7], action recognition [8], visual saliency detection [9], and scene labeling [10]. However, more novel technologies are necessary to further enhance the overall detection efficiency.…”

Section: Introductionmentioning

confidence: 99%

An Object Detection Framework Based on Deep Features and High-Quality Object Locations

Guan¹,

Aamir²,

Hu³

et al. 2021

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Objection detection has long been a fundamental issue in computer vision. Despite being widely studied, it remains a challenging task in the current body of knowledge. Many researchers are eager to develop a more robust and efficient mechanism for object detection. In the extant literature, promising results are achieved by many novel approaches of object detection and classification. However, there is ample room to further enhance the detection efficiency. Therefore, this paper proposes an image object detection and classification, using a deep neural network (DNN) for based on high-quality object locations. The proposed method firstly derives high-quality class-independent object proposals (locations) through computing multiple hierarchical segments with super pixels. Next, the proposals were ranked by region score, i.e., several contours wholly enclosed in the proposed region. After that, the top-ranking object proposal was adopted for post-classification by the DNN. During the post-classification, the network extracts the eigenvectors from the proposals, and then maps the features with the softmax classifier, thereby determining the class of each object. The proposed method was found superior to traditional approaches through an evaluation on Pascal VOC 2007 Dataset.

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Detection and Tracking of Motorcycles in Congested Urban Environments Using Deep Learning and Markov Decision Processes

Cited by 5 publications

References 17 publications

Real-Time Motorbike Detection: AI on the Edge Perspective

Real-Time Motorbike Detection: AI on the Edge Perspective

Multiple Object Tracking for Robust Quantitative Analysis of Passenger Motion While Boarding and Alighting a Metropolitan Train

An Object Detection Framework Based on Deep Features and High-Quality Object Locations

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