Length-based vehicle classification using images from uncalibrated video cameras

Avery, Ryan Patrick; Wang, Y.; Rutherford, G S

doi:10.1109/itsc.2004.1398994

Cited by 62 publications

(28 citation statements)

References 4 publications

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“…Avery, Wang and Rutherford proposed digital image processing algorithm for length based vehicle classification. They used streams of images captured from un-calibrated video camera [12]. They actually compare the length of different vehicles in order to estimate the truck volumes and eliminated the needs of different complex calibration systems.…”

Section: Related Workmentioning

confidence: 99%

Classification and Counting of Vehicles for Traffic Surveillance

S¹

2018

IJRASET

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Traffic surveillance is a system used to observe the traffic congestion of a specific area. The surveillance system involves detection of moving vehicles, counting the number of vehicles and the classification of the detected vehicles. Recognition of the moving vehicles is the initial step which can be carried out either in each and every frame based on background modeling with discrete cosine transform (DCT). The proposed method is an efficient approach for vehicle segmentation based on texture analysis, a 2D discrete cosine transform (DCT) which is utilized to extract texture features in each image block. By this method the foreground object is detected by eliminating the background, vehicles are recognized based on their size and highlighted by drawing a bounding box around them. The actual count of the vehicles is estimated by counting the number of bounding box present in the video. Further the detected vehicles present in the video can be classified into car, bike, truck etc. based on the SVM classifiers. Support vector machine (SVM) are supervised learning tools which is applied for data classification and regression, It maps the training samples that are the points in features space into different categories which are clearly separated with the widest gap in between them. The proposed system can be used to conduct a survey and allow the users to monitor the condition of traffic flow by counting the number of vehicles passing through a specific location.

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Section: Related Workmentioning

confidence: 99%

Classification and Counting of Vehicles for Traffic Surveillance

S¹

2018

IJRASET

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“…Where T denotes the preset threshold and It (i,j) and j) are pixels at current and previous frames (Jianxin et al 2003) and (Avery et al 2004). Noted that the difference of two corresponding pixels in the subsequent frames is the main problem with this simple approach.…”

Section: Theoretical Work Pixel Analysismentioning

confidence: 99%

Developing and Validating a Simulation Model for Counting and Classification of Vehicles

Jehad¹,

Rahmat²

2016

JKUKM

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“…Avery, Wang and Rutherford [2] proposed digital image processing algorithm for length based vehicle classification. They used streams of images captured from un-calibrated video camera.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative Analysis of Automatic Vehicle Classification Techniques: A Survey

Yousaf¹,

Iftikhar²,

Javed

2012

IJIGSP

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Abstract-Vehicle classification has emerged as a significant field of study because of its importance in variety of applications like surveillance, security system, traffic congestion avoidance and accidents prevention etc. So far numerous algorithms have been implemented for classifying vehicle. Each algorithm follows different procedures for detecting vehicles from videos. By evaluating some of the commonly used techniques we highlighted most beneficial methodology for classifying vehicles. In this paper we pointed out the working of several video based vehicle classification algorithms and compare these algorithms on the basis of different performance metrics such as classifiers, classification methodology or principles and vehicle detection ratio etc. After comparing these parameters we concluded that Hybrid Dynamic Bayesian Network (HDBN) Classification algorithm is far better than the other algorithms due to its nature of estimating the simplest features of vehicles from different videos. HDBN detects vehicles by following important stages of feature extraction, selection and classification. It extracts the rear view information of vehicles rather than other information such as distance between the wheels and height of wheel etc.

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Length-based vehicle classification using images from uncalibrated video cameras

Cited by 62 publications

References 4 publications

Classification and Counting of Vehicles for Traffic Surveillance

Classification and Counting of Vehicles for Traffic Surveillance

Developing and Validating a Simulation Model for Counting and Classification of Vehicles

Comparative Analysis of Automatic Vehicle Classification Techniques: A Survey

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