Pedestrian detection and vehicle type recognition using CENTROG features for nighttime thermal images

Irhebhude, Martins E.; Ali, Mansoor; Edirisinghe, Eran A.

doi:10.1109/iccp.2015.7312693

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…Instead of Radon Transform features, Martins et al [15] used a combination of Gray Level Co-occurrence Matrix (GLCM), Correlation-based Feature Selection and speedup robust fast features (SURF) to recognise military uniforms. This approach achieved an accuracy of 94%.…”

Section: Use Of Radon Transformmentioning

confidence: 99%

Automated uniform recognition to enhance video surveillance at correctional services in South Africa

Kunene,

Zandamela,

Mabuza-Hocquet

et al. 2023

MATEC Web Conf.

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Injuries from inmate altercations are common in correctional service facilities. Monitoring incidents manually from video surveillance can be challenging. Computer vision has the potential to assist security personnel in securing facilities. This work compares two methods for recognising occupational uniforms with the aim of improving situational awareness and safety in prisons. The first method uses histograms of hue and saturation (HS) colour-space features and a shallow learning classifier. The second method uses convolutional neural network (CNN) models trained with either hand-engineered or automatically learned features. A training dataset with civilians, South African correctional service and police uniforms was created. The experimental results demonstrate comparable performance from shallow learning algorithms and CNN models. Machine learning algorithms evaluated on the proposed colour features achieved an average balanced performance (F1-score) of 0.85 and inference times range from 0.01 to 4.9 milliseconds.

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Section: Use Of Radon Transformmentioning

confidence: 99%

Automated uniform recognition to enhance video surveillance at correctional services in South Africa

Kunene,

Zandamela,

Mabuza-Hocquet

et al. 2023

MATEC Web Conf.

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“…Classification of vehicle types using thermal camera performed poorly since a thermal camera shows features in pseudo colours based on generated heat. In another similar study, HOG and SVM classifiers were implemented to detect moving vehicles using thermal camera [20]. Vehicle category classification using a thermal camera was performed by heat distribution analysis in another study [21].…”

Section: Literature Reviewmentioning

confidence: 99%

Deep learning‐based vehicle occupancy detection in an open parking lot using thermal camera

Paidi

Fleyeh

Nyberg

2020

IET intell. transp. syst

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Parking vehicle is a daunting task and a common problem in many cities around the globe. The search for parking space leads to congestion, frustration and increased air pollution. Information of a vacant parking space would facilitate to reduce congestion and subsequent air pollution. Therefore, aim of the paper is to acquire vehicle occupancy in an open parking lot using deep learning. Thermal camera was used to collect the data during varying environmental conditions such as; sunny, dusk, dawn, dark and snowy conditions. Vehicle detection with deep learning was implemented where image classification and object localization were performed for multi object detection. The dataset consists of 527 images which were manually labelled as there were no pre-labelled thermal images available. Multiple deep learning networks such as Yolo, ReNet18, ResNet50 and GoogleNet with varying layers and architectures were evaluated on vehicle detection. Yolo, GoogleNet and ResNet18 are computationally efficient detectors which took less processing time while Resnet50 produced better detection results compared to other detectors. However, ResNet18 also produced minimal miss rates and is suitable for real time vehicle detection. The detected results were compared with a template of parking spaces and IoU value is used to identify vehicle occupancy information.

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Deep Learning-Based Vehicle Type and Color Classification to Support Safe Autonomous Driving

Kim

2024

Applied Sciences

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This technology can prevent accidents involving large vehicles, such as trucks or buses, by selecting an optimal driving lane for safe autonomous driving. This paper proposes a method for detecting forward-driving vehicles within road images obtained from a vehicle’s DashCam. The proposed method also classifies the types and colors of the detected vehicles. The proposed method uses a YOLO deep learning network for vehicle detection based on a pre-trained ResNet-50 convolutional neural network. Additionally, a Resnet-50 CNN-based object classifier, using transfer learning, was used to classify vehicle types and colors. Vehicle types were classified into four categories based on size whereas vehicle colors were classified into eight categories. During autonomous driving, vehicle types are used to determine driving lanes, whereas vehicle colors are used to distinguish the road infrastructure, such as lanes, vehicles, roads, backgrounds, and buildings. The datasets used for learning consisted of road images acquired in various driving environments. The proposed method achieved a vehicle detection accuracy of 91.5%, vehicle type classification accuracy of 93.9%, and vehicle color classification accuracy of 94.2%. It accurately detected vehicles and classified their types and colors. These can be applied to autonomous and safe driving support systems to enhance the safety of autonomous vehicles.

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Pedestrian detection and vehicle type recognition using CENTROG features for nighttime thermal images

Cited by 3 publications

References 17 publications

Automated uniform recognition to enhance video surveillance at correctional services in South Africa

Automated uniform recognition to enhance video surveillance at correctional services in South Africa

Deep learning‐based vehicle occupancy detection in an open parking lot using thermal camera

Deep Learning-Based Vehicle Type and Color Classification to Support Safe Autonomous Driving

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