Vehicle classification based on images from visible light and thermal cameras

Nam, Yunyoung; Nam, Yun-Cheol

doi:10.1186/s13640-018-0245-2

Cited by 45 publications

(24 citation statements)

References 24 publications

(22 reference statements)

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“…However, since vehicles are moving on the road, heat can be captured by the windshield or tyres which might not be applicable to this paper, as the vehicles are stationary in a parking lot. Based on another study, the thermal camera was used to classify vehicles types using features extracted from the front end of the vehicle [11]. Classification of vehicle types using thermal camera performed poorly since a thermal camera shows features in pseudo colours based on generated heat.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Thermal cameras were largely used on images or videos collected during dusk or night conditions [8]. A combination of visual spectrum images for daylight and thermal images for night conditions were utilised to enable detection in varying illumination conditions [11]. There is scarce of the literature identifying vehicles only using the thermal camera in varying illumination and environmental conditions [12].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

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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“…Experimental results obtained shows 92.5% accuracy over a testing dataset. Nam and Nam (2018) had proposed a surveillance system for detecting and classifying of vehicles during the day and at night time. The various parameters used for the feature extraction was textures, entropy, homogeneity, energy and contrast.…”

Section: Related Workmentioning

confidence: 99%

Object Detection and Classification from Thermal Images Using Region based Convolutional Neural Network

Mittal¹,

Srivastava²,

Chawla³

2019

Journal of Computer Science

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In recent years, object detection and classification has gained so much popularity in different application areas like face detection, selfdriving cars, pedestrian detection, security surveillance systems etc. The traditional detection methods like background subtraction, Gaussian Mixture Model (GMM), Support Vector Machine (SVM) have certain drawbacks like overlapping of objects, distortion due to smoke, fog, lightening conditions etc. In this paper, thermal images are used as thermal cameras capture the image by using the heat generated by the objects. Thermal camera images are not influenced by smoke and bad weather conditions which makes them a built-up apparatus in inquiry and safeguards or fire-fighting applications. These days, deep learning techniques are extensively used for detection and classification. In this paper, a comparative analysis has been done by applying Faster region based convolutional neural network on thermal images and visual spectrum images. The experimental results show that thermal camera images are better as compared to visible spectrum images.

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“…For automobile tracking based on surveillance video, it is essential to extract the primary vehicle features such as plate number, color, and size from a video frame. Nam et al [8] classified the types of vehicles as, amongst others, SUVs, sedans, and RVs using images from visible light and thermal cameras. Suryatali et al [9] reported a scheme for determining the direction and size of automobiles using Kalman filters.…”

Section: Automobile Recognizing and Trackingmentioning

confidence: 99%

Real-time car tracking system based on surveillance videos

Jung

Kim²,

Hwang

2018

J Image Video Proc.

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As a variety of video surveillance devices such as CCTV, drones, and car dashboard cameras have become popular, numerous studies have been conducted regarding the effective enforcement of security and surveillance based on video analysis. In particular, in car-related surveillance, car tracking is the most challenging task. One early approach to accomplish such a task was to analyze frames from different video sources separately. Considering the shooting range of the bulk of video devices, the outcome from the analysis of single video source is highly limited. To obtain more comprehensive information for car tacking, a set of video sources should be considered together and the relevant information should be integrated according to spatial and temporal constraints. Therefore, in this study, we propose a real-time car tracking system based on surveillance videos from diverse devices including CCTV, dashboard cameras, and drones. For scalability and fault tolerance, our system is built on a distributed processing framework and comprises a Frame Distributor, a Feature Extractor, and an Information Manager. The Frame Distributor is responsible for distributing the video frames from various devices to the processing nodes. The Feature Extractor extracts principal vehicle features such as plate number, location, and time from each frame. The Information Manager stores all the features into a database and handles user requests by collecting relevant information from the feature database. To illustrate the effectiveness of our proposed system, we implemented a prototype system and performed a number of experiments. We report some of the results.

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Vehicle classification based on images from visible light and thermal cameras

Cited by 45 publications

References 24 publications

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

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

Object Detection and Classification from Thermal Images Using Region based Convolutional Neural Network

Real-time car tracking system based on surveillance videos

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