Real-time Pedestrian Tracking based on Deep Features

Bhola, Geetanjali; Kathuria, Akhil; Kumar, Deepak; Das, Chandan Jyoti

doi:10.1109/iciccs48265.2020.9121061

Cited by 7 publications

(2 citation statements)

References 11 publications

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“…The detection and subsequent monitoring of pedestrians in the driving scene allows the automatic driver assistance system to continuously validate if the driving dynamics and the level of attention are compatible with the presence of pedestrians in the scene. Many authors have investigated this relevant issue by analyzing the advantages inherent in the use of deep learning architectures (Tian et al, 2015;Song et al, 2018;Jeon et al, 2019;Bhola et al, 2020). The authors investigated several interesting object detection and tracking architecture backbones to be adapted to pedestrian tracking.…”

Section: The Deep Network With Criss-cross Attention For Pedestrian Tracking Systemmentioning

confidence: 99%

Deep Neuro-Vision Embedded Architecture for Safety Assessment in Perceptive Advanced Driver Assistance Systems: The Pedestrian Tracking System Use-Case

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et al. 2021

Front. Neuroinform.

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In recent years, the automotive field has been changed by the accelerated rise of new technologies. Specifically, autonomous driving has revolutionized the car manufacturer's approach to design the advanced systems compliant to vehicle environments. As a result, there is a growing demand for the development of intelligent technology in order to make modern vehicles safer and smarter. The impact of such technologies has led to the development of the so-called Advanced Driver Assistance Systems (ADAS), suitable to maintain control of the vehicle in order to avoid potentially dangerous situations while driving. Several studies confirmed that an inadequate driver's physiological condition could compromise the ability to drive safely. For this reason, assessing the car driver's physiological status has become one of the primary targets of the automotive research and development. Although a large number of efforts has been made by researchers to design safety-assessment applications based on the detection of physiological signals, embedding them into a car environment represents a challenging task. These mentioned implications triggered the development of this study in which we proposed an innovative pipeline, that through a combined less invasive Neuro-Visual approach, is able to reconstruct the car driver's physiological status. Specifically, the proposed contribution refers to the sampling and processing of the driver PhotoPlethysmoGraphic (PPG) signal. A parallel enhanced low frame-rate motion magnification algorithm is used to reconstruct such features of the driver's PhotoPlethysmoGraphic (PPG) data when that signal is no longer available from the native embedded sensor platform. A parallel monitoring of the driver's blood pressure levels from the PPG signal as well as the driver's eyes dynamics completes the reconstruction of the driver's physiological status. The proposed pipeline has been tested in one of the major investigated automotive scenarios i.e., the detection and monitoring of pedestrians while driving (pedestrian tracking). The collected performance results confirmed the effectiveness of the proposed approach.

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Section: The Deep Network With Criss-cross Attention For Pedestrian Tracking Systemmentioning

confidence: 99%

Deep Neuro-Vision Embedded Architecture for Safety Assessment in Perceptive Advanced Driver Assistance Systems: The Pedestrian Tracking System Use-Case

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Spampinato

et al. 2021

Front. Neuroinform.

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“…Existing state-of-the-art approaches for pedestrian tracking in autonomous vehicles predominantly rely on object detection and tracking algorithms, including Faster R-CNN (Region-based Convolutional Neural Network), YOLO (You Only Look Once) and SORT algorithms [1]. However, these methods encounter issues in scenarios where pedestrians are occluded or only partially visible [2]. These challenges serve as the impetus for our research, as we strive to enhance the reliability and effectiveness of pedestrian tracking, particularly in complex scenarios.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Pedestrian Tracking in Autonomous Vehicles by Using Advanced Deep Learning Techniques

Sukkar,

Shukla,

Kumar

et al. 2024

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Effective collision risk reduction in autonomous vehicles relies on robust and straightforward pedestrian tracking. Challenges posed by occlusion and switching scenarios significantly impede the reliability of pedestrian tracking. In the current study, we strive to enhance the reliability and also the efficacy of pedestrian tracking in complex scenarios. Particularly, we introduce a new pedestrian tracking algorithm that leverages both the YOLOv8 (You Only Look Once) object detector technique and the StrongSORT algorithm, which is an advanced deep learning multi-object tracking (MOT) method. Our findings demonstrate that StrongSORT, an enhanced version of the DeepSORT MOT algorithm, substantially improves tracking accuracy through meticulous hyperparameter tuning. Overall, the experimental results reveal that the proposed algorithm is an effective and efficient method for pedestrian tracking, particularly in complex scenarios encountered in the MOT16 and MOT17 datasets. The combined use of Yolov8 and StrongSORT contributes to enhanced tracking results, emphasizing the synergistic relationship between detection and tracking modules.

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A review of vision-based indoor HAR: state-of-the-art, challenges, and future prospects

Bhola

Vishwakarma

2023

Multimed Tools Appl

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With the advent of technology, we are getting more comfortable with the use of gadgets, cameras, etc., and find Artificial Intelligence as an integral part of most of the tasks we perform throughout the day. In such a scenario, the use of cameras and vision-based sensors comes as an escape from many real-time problems and challenges. One major application of these vision-based systems is Indoor Human Activity Recognition (HAR) which serves in a variety of scenarios ranging from smart homes, elderly care, assisted living, and human behavior pattern analysis for identifying any abnormal behavior to abnormal activity recognition like falling, slipping, domestic violence, etc. The effect of HAR in real time has made the area of indoor activity recognition a more explored zone by the industrial segment to attract users with their products in multiple domains. Hence, considering these aspects of HAR, this work proposes a detailed survey on indoor HAR. Through this work, we have highlighted the recent methodologies and their performance in the field of indoor activity recognition. We have also discussed- the challenges, detailed study of approaches with real-world applications of indoor-HAR, datasets available for indoor activity, and their technical details in this work. We have proposed a taxonomy for indoor HAR and highlighted the state-of-the-art and future prospects by mentioning the research gaps and the shortcomings of recent surveys with respect to our work.

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Real-time Pedestrian Tracking based on Deep Features

Cited by 7 publications

References 11 publications

Deep Neuro-Vision Embedded Architecture for Safety Assessment in Perceptive Advanced Driver Assistance Systems: The Pedestrian Tracking System Use-Case

Deep Neuro-Vision Embedded Architecture for Safety Assessment in Perceptive Advanced Driver Assistance Systems: The Pedestrian Tracking System Use-Case

Enhancing Pedestrian Tracking in Autonomous Vehicles by Using Advanced Deep Learning Techniques

A review of vision-based indoor HAR: state-of-the-art, challenges, and future prospects

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