Hardware implementation of real-time pedestrian detection system

Helali, Abdelhamid; Ameur, Haythem; Górriz, Juan Manuel; Ramı́rez, Javier; Maâref, H.

doi:10.1007/s00521-020-04731-y

Cited by 15 publications

(8 citation statements)

References 26 publications

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“…Deep Learning is now widely used in video and image analysis in a variety of research areas, ranging from medical diagnostics [19], [20], financial forecasting [21], [22] and cybersecurity [23], and over the years has also had a positive impact on road safety [24]. The state of the art presents considerable scientific solutions to the problem of road safety, providing inspiration for the development of methods for pedestrian detection [25], vehicle tracking [26], and accident detection [27]. The solution proposed for this project, compared to the state-of-the-art, presents several interesting innovations, introduced with the aim of creating a safe and safeguarded environment for vehicles and pedestrians.…”

Section: State Of the Art Of The Current Systemsmentioning

confidence: 99%

Heimdall: an AI-based infrastructure for traffic monitoring and anomalies detection

Atzori¹,

Barra²,

Carta³

et al. 2021

Preprint

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Since their appearance, Smart Cities have aimed at improving the daily life of people, helping to make public services smarter and more efficient. Several of these services are often intended to provide better security conditions for citizens and drivers. In this vein, we present HEIMDALL, an AI-based video surveillance system for traffic monitoring and anomalies detection. The proposed system features three main tiers: a ground level, consisting of a set of smart lampposts equipped with cameras and sensors, and an advanced AI unit for detecting accidents and traffic anomalies in real time; a territorial level, which integrates and combines the information collected from the different lampposts, and cross-correlates it with external data sources, in order to coordinate and handle warnings and alerts; a training level, in charge of continuously improving the accuracy of the modules that have to sense the environment. Finally, we propose and discuss an early experimental approach for the detection of anomalies, based on a Faster R-CNN, and adopted in the proposed infrastructure.

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Section: State Of the Art Of The Current Systemsmentioning

confidence: 99%

Heimdall: an AI-based infrastructure for traffic monitoring and anomalies detection

Atzori¹,

Barra²,

Carta³

et al. 2021

Preprint

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“…Higher resolution cameras facilitate the detection task and allow the system to detect distant pedestrians, as they will be encoded in a greater number of pixels. For example, in [13] the authors claim that increasing the resolution from 480 × 320 to 1920 × 1080 pixels allows for increasing the detection distance by 77 m. In addition, a higher frame rate improves obstacle tracking, reduces latency, and minimizes the false detection rate [13].…”

Section: Introductionmentioning

confidence: 99%

“…For example, in [14] the authors process images in resolution 416 × 416 at 60 FPS. On the other hand, solutions that combine a histogram of oriented gradients (HOG) with shallow linear classifiers such as support vector machines (SVMs) can achieve high processing rates [13] due to their relatively regular and straightforward processing.…”

Section: Introductionmentioning

confidence: 99%

“…Due to the excellent parallelism capabilities of the field-programmable gate array (FPGA) platforms [16][17][18], some architectures for HOG-SVM-based detectors have been proposed. The proposed detectors in [13,19] do not support the image pyramid, which is one of the fundamental approaches to locating pedestrians nearer to the image. They propose approximations to avoid using the arc-tangent and so as to not perform the linear interpolation in the cell computation that reduces detection accuracy [20].…”

Section: Introductionmentioning

confidence: 99%

“…Thus, this work proposes an FPGA-based processing architecture of our detector [6] to further enhance the PCP system processing performance. The proposed detector is similar to the works [13,19,22], but does not approximate the cell calculation nor adopt a double frequency strategy. In addition to the pyramid, the detector supports processing windows of different sizes.…”

Section: Introductionmentioning

confidence: 99%

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FPGA-Based Pedestrian Detection for Collision Prediction System

Cambuim

Barros

2022

Sensors

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Pedestrian detection (PD) systems capable of locating pedestrians over large distances and locating them faster are needed in Pedestrian Collision Prediction (PCP) systems to increase the decision-making distance. This paper proposes a performance-optimized FPGA implementation of a HOG-SVM-based PD system with support for image pyramids and detection windows of different sizes to locate near and far pedestrians. This work proposes a hardware architecture that can process one pixel per clock cycle by exploring data and temporal parallelism using techniques such as pipeline and spatial division of data between parallel processing units. The proposed architecture for the PD module was validated in FPGA and integrated with the stereo semi-global matching (SGM) module, also prototyped in FPGA. Processing two windows of different dimensions permitted a reduction in miss rate of at least 6% compared to a uniquely sized window detector. The performances achieved by the PD system and the PCP system in HD resolution were 100 and 66.2 frames per second (FPS), respectively. The performance improvement achieved by the PCP system with the addition of our PD module permitted an increase in decision-making distance of 3.3 m compared to a PCP system that processes at 30 FPS.

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