A Survey of Pedestrian Detection in Video

Solichin, Achmad; Harjoko, Agus; Putra, Agfianto Eko

doi:10.14569/ijacsa.2014.051007

Cited by 7 publications

(6 citation statements)

References 85 publications

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“…Detection techniques, especially for pedestrians, has been widely researched with several techniques. The first instance of object detection is known as the region of interest (ROI) [28]. Once the potential location of the desired object (i.e., pedestrian or cyclist) is identified in an image, feature extraction takes place.…”

Section: Detection Techniques: a Brief Historymentioning

confidence: 99%

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Pedestrian and Cyclist Detection and Intent Estimation for Autonomous Vehicles: A Survey

et al. 2019

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As autonomous vehicles become more common on the roads, their advancement draws on safety concerns for vulnerable road users, such as pedestrians and cyclists. This paper presents a review of recent developments in pedestrian and cyclist detection and intent estimation to increase the safety of autonomous vehicles, for both the driver and other road users. Understanding the intentions of the pedestrian/cyclist enables the self-driving vehicle to take actions to avoid incidents. To make this possible, development of methods/techniques, such as deep learning (DL), for the autonomous vehicle will be explored. For example, the development of pedestrian detection has been significantly advanced using DL approaches, such as; Fast Region-Convolutional Neural Network (R-CNN) , Faster R-CNN and Single Shot Detector (SSD). Although DL has been around for several decades, the hardware to realise the techniques have only recently become viable. Using these DL methods for pedestrian and cyclist detection and applying it for the tracking, motion modelling and pose estimation can allow for a successful and accurate method of intent estimation for the vulnerable road users. Although there has been a growth in research surrounding the study of pedestrian detection using vision-based approaches, further attention should include focus on cyclist detection. To further improve safety for these vulnerable road users (VRUs), approaches such as sensor fusion and intent estimation should be investigated.

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Section: Detection Techniques: a Brief Historymentioning

confidence: 99%

“…These features can include edges, shapes, curvature, etc. These features are sent to a classifier for classification [28] (see Figure 1).…”

Section: Detection Techniques: a Brief Historymentioning

confidence: 99%

Pedestrian and Cyclist Detection and Intent Estimation for Autonomous Vehicles: A Survey

et al. 2019

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“…Ten years ago, the detection of road users and, particularly, pedestrians were mainly based on Histograms of Oriented Gradient (HOG) [12,13]. Nowadays, artificial neural networks and, more specifically, Convolutional Neural Networks are used for those tasks.…”

Section: Road User Detectionmentioning

confidence: 99%

Road User Position and Speed Estimation via Deep Learning from Calibrated Fisheye Videos

Berviller

Ansarnia

Tisserand

et al. 2023

Sensors

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In this paper, we present a deep learning processing flow aimed at Advanced Driving Assistance Systems (ADASs) for urban road users. We use a fine analysis of the optical setup of a fisheye camera and present a detailed procedure to obtain Global Navigation Satellite System (GNSS) coordinates along with the speed of the moving objects. The camera to world transform incorporates the lens distortion function. YOLOv4, re-trained with ortho-photographic fisheye images, provides road user detection. All the information extracted from the image by our system represents a small payload and can easily be broadcast to the road users. The results show that our system is able to properly classify and localize the detected objects in real time, even in low-light-illumination conditions. For an effective observation area of 20 m × 50 m, the error of the localization is in the order of one meter. Although an estimation of the velocities of the detected objects is carried out by offline processing with the FlowNet2 algorithm, the accuracy is quite good, with an error below one meter per second for urban speed range (0 to 15 m/s). Moreover, the almost ortho-photographic configuration of the imaging system ensures that the anonymity of all street users is guaranteed.

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“…In summary, although HOG feature alone remains a competitive descriptor [8,15,42] for pedestrian detection, researchers have found advantages in combining this robust feature with others under various scenarios albeit at the cost of even more computational complexity than HOG alone poses.…”

Section: A Feature Vector Extractionmentioning

confidence: 99%

“…For instance ACF achieves as low as 17% miss rate on INRIA dataset by employing multiple feature channels with boosted classifiers while Crosstalk achieves as high as 45 fps processing speed through feature re-use and extensive reliance on vector processing support from Intel processors [50,51]. However, no single feature-classifier pair has been reported in literature which is better discriminant as well as computationally less complex than HOG as noted by Dollar et al [8], Rodrigo et al [15], Achmad et al [42] and Yong et al [37]. Hence, in the recent years many dedicated hardware designs for object detection have adopted HOG-Linear SVM despite its computational complexity [53][54][55][56][57][58][59][60].…”

Section: Real-time Performance Of Pedestrian Detectorsmentioning

confidence: 99%

A Low-Complexity Pedestrian Detection Framework for Smart Video Surveillance Systems

Bilal

Khan

et al. 2017

IEEE Trans. Circuits Syst. Video Technol.

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Pedestrian detection is a key problem in computer vision and is currently addressed with increasingly complex solutions involving compute-intensive features and classification schemes. In this scope, Histogram of Oriented Gradients (HOG) in conjunction with linear SVM classifier is considered to be the single most discriminative feature which has been adopted as a stand-alone detector as well as a key instrument in advance systems involving hybrid features and cascaded detectors. In this paper, we propose a pedestrian detection framework which is computationally less expensive as well as more accurate than HOG-linear SVM. The proposed scheme exploits the discriminating power of the locally significant gradients in building orientation histograms without involving complex floating point operations while computing the feature. The integeronly feature allows the use of powerful Histogram Intersection Kernel SVM classifier in a fast look-up table based implementation. Resultantly, the proposed framework achieves at least 3% more accurate detection results than HOG on standard datasets while being 1.8 and 2.6 times faster on conventional desktop PC and embedded ARM platforms respectively for a single scale pedestrian detection on VGA resolution video. Additionally, hardware implementation on Altera Cyclone IV FPGA results in more than 40% savings in logic resources compared to its HOG-Linear SVM competitor. Hence, the proposed feature and classification setup is shown to be a better candidate as the single most discriminative pedestrian detector than currently accepted HOG-linear SVM.

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A Survey of Pedestrian Detection in Video

Cited by 7 publications

References 85 publications

Pedestrian and Cyclist Detection and Intent Estimation for Autonomous Vehicles: A Survey

Pedestrian and Cyclist Detection and Intent Estimation for Autonomous Vehicles: A Survey

Road User Position and Speed Estimation via Deep Learning from Calibrated Fisheye Videos

A Low-Complexity Pedestrian Detection Framework for Smart Video Surveillance Systems

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