Tuning of Classifiers to Speed-Up Detection of Pedestrians in Infrared Images

Piniarski, Karol; Pawlowski, Pawel; Dąbrowski, Adam

doi:10.3390/s20164363

Cited by 5 publications

(2 citation statements)

References 48 publications

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“…Meanwhile, Piniarski et al [19] proposed a method to speed up pedestrian detection in infrared images, resulting in a 74% reduction in classification time. The classifiers analyzed in this study were a HOG with an SVM, an aggregate channel feature, and a deep CNN.…”

Section: Literature Reviewmentioning

confidence: 99%

Convolutional Neural Network for Overcrowded Public Transportation Pickup Truck Detection

Suttanuruk¹,

Jomnonkwao²,

Ratanavaraha³

et al. 2023

Computers, Materials &Amp; Continua

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Thailand has been on the World Health Organization (WHO)'s notorious deadliest road list for several years, currently ranking eighth on the list. Among all types of road fatalities, pickup trucks converted into vehicles for public transportation are found to be the most problematic due to their high occupancy and minimal passenger safety measures, such as safety belts. Passenger overloading is illegal, but it is often overlooked. The country often uses police checkpoints to enforce traffic laws. However, there are few or no highway patrols to apprehend offending drivers. Therefore, in this study, we propose the use of existing closed-circuit television (CCTV) traffic cameras with deep learning techniques to classify overloaded public transport pickup trucks (PTPT) to help reduce accidents. As the said type of vehicle and its passenger occupancy characteristics are unique, a new model is deemed necessary. The contributions of this study are as follows: First, we used various state-of-the-art object detection YOLOv5 (You Only Look Once) models to obtain the optimum overcrowded model pretrained on our manually labeled dataset. Second, we made our custom dataset available. Upon investigation, we compared all the latest YOLOv5 models and discovered that the YOLOv5L yielded the optimal performance with a mean average precision (mAP) of 95.1% and an inference time of 33 frames per second (FPS) on a graphic processing unit (GPU). We aim to deploy the selected model on traffic control computers to alert the police of such passenger-overloading violations. The use of a chosen algorithm is feasible and is expected to help reduce trafficrelated fatalities.

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Section: Literature Reviewmentioning

confidence: 99%

Convolutional Neural Network for Overcrowded Public Transportation Pickup Truck Detection

Suttanuruk¹,

Jomnonkwao²,

Ratanavaraha³

et al. 2023

Computers, Materials &Amp; Continua

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“…For this reason, several studies are currently focused on trying to solve this problem. Some of the solutions being worked on are based on improving pedestrian detection algorithms in this type of low visibility environment [ 29 , 30 , 31 ], and others focus on using other types of systems to obtain information, such as the use of LIDAR [ 32 , 33 ] or infrared sensors [ 34 , 35 ], or on fusing images obtained from the classic RGB camera with other detection systems, such as thermal cameras [ 36 ], LIDAR [ 37 ], or an array of microphones [ 38 ].…”

Section: Introductionmentioning

confidence: 99%

Feasibility of Using a MEMS Microphone Array for Pedestrian Detection in an Autonomous Emergency Braking System

Izquierdo

Val

Villacorta

2021

Sensors

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Pedestrian detection by a car is typically performed using camera, LIDAR, or RADAR-based systems. The first two systems, based on the propagation of light, do not work in foggy or poor visibility environments, and the latter are expensive and the probability associated with their ability to detect people is low. It is necessary to develop systems that are not based on light propagation, with reduced cost and with a high detection probability for pedestrians. This work presents a new sensor that satisfies these three requirements. An active sound system, with a sensor based on a 2D array of MEMS microphones, working in the 14 kHz to 21 kHz band, has been developed. The architecture of the system is based on an FPGA and a multicore processor that allow the system to operate in real time. The algorithms developed are based on a beamformer, range and lane filters, and a CFAR (Constant False Alarm Rate) detector. In this work, tests have been carried out with different people and in different ranges, calculating, in each case and globally, the Detection Probability and the False Alarm Probability of the system. The results obtained verify that the developed system allows the detection and estimation of the position of pedestrians, ensuring that a vehicle travelling at up to 50 km/h can stop and avoid a collision.

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