Performance Analysis of Vehicle Detection Techniques: A Concise Survey

Hanif, Adnan; Mansoor, Asif; Imran, Ali Shariq

doi:10.1007/978-3-319-77712-2_46

Cited by 6 publications

(6 citation statements)

References 38 publications

(34 reference statements)

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“…These methods have been applied to land slide, crops, urban land change and forests [13][14][15] [16]. Figure 4 shows the processes whereby an input image goes through a hypothesis generation phase, the hypothesis is validated and tested using the established knowledge and rules.…”

Section: (C) Knowledge Based Detection Methodsmentioning

confidence: 99%

Machine Learning Methods for Forest Image Analysis and Classification: A Survey of the State of the Art

2022

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The advent of modern remote sensors alongside the development of advanced parallel computing has significantly transformed both the theoretical and real implementation aspects of remote sensing. Several algorithms for detecting objects of interest in remote sensing images and subsequent classification have been devised, and these include template matching based methods, machine learning and knowledge-based methods. Knowledge-driven approaches have received much attention from the remote sensing fraternity. They do, however, face challenges in terms of sensory gap, duality of expression, vagueness and ambiguity, geographic concepts expressed in multiple modes, and semantic gap. This paper aims to review and provide an up-to-date survey on machine learning and knowledge driven approaches towards remote sensing forest image analysis. It is envisaged that this work will assist researchers in coming up with efficient models that accurately detect and classify forest images. There is a mismatch between what domain experts expect from remote sensing data and what remote sensing science produces. Such a mismatch or disparity can be reduced or alleviated by adopting an ontology paradigm methodology. Ontologies should be used to support the future of remote sensing in forest object classification. The paper is presented in five parts: (1) a review of methods used for forest image detection and classification; (2) challenges faced by object detection methods; (3) analysis of segmentation techniques employed; (4) feature extraction and classification; and (5) performance of the state-of-the-art methods employed in forest image detection and classification.

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Section: (C) Knowledge Based Detection Methodsmentioning

confidence: 99%

Machine Learning Methods for Forest Image Analysis and Classification: A Survey of the State of the Art

2022

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“…is is an inevitable result of the emergence of advanced image processing and AI technologies. Based on the survey in [11], we may divide the vision-based vehicle counting method into two categories: regression-based methods and detectionbased methods.…”

Section: Related Workmentioning

confidence: 99%

Towards AI-Based Traffic Counting System with Edge Computing

Dinh

Nguyen

Thai

et al. 2021

Journal of Advanced Transportation

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The recent years have witnessed a considerable rise in the number of vehicles, which has placed transportation infrastructure and traffic control under tremendous pressure. Yielding timely and accurate traffic flow information is essential in the development of traffic control strategies. Despite the continual advances and the wealth of literature available in intelligent transportation system (ITS), there is a lack of practical traffic counting system, which is readily deployable on edge devices. In this study, we introduce a low-cost and effective edge-based system integrating object detection models to perform vehicle detecting, tracking, and counting. First, a vehicle detection dataset (VDD) representing traffic conditions in Vietnam was created. Several deep learning models for VDD were then examined on two different edge device types. Using this detection, we presented a lightweight counting method seamlessly combining with a traditional tracking method to increase counting accuracy. Finally, the traffic flow information is obtained based on counted vehicle categories and their directions. The experiment results clearly indicate that the proposed system achieves the top inference speed at around 26.8 frames per second (FPS) with 92.1% accuracy on the VDD. This proves that our proposal is capable of producing high-accuracy traffic flow information and can be applicable to ITS in order to reduce labor-intensive tasks in traffic management.

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“…(iii)Sensitivity (Recall or True positive rate): Sensitivity is nothing but the ratio of the number of vehicles correctly detected to the actual number of vehicles [21].…”

Section: B Performance Measurements (I)accuracymentioning

confidence: 99%

Development of Vehicle Speed Estimation Technique using Image Processing

Raikar¹,

Arakeri²

2020

IJERT

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In India, the surveillance system and intelligent traffic management is the essential requirement for smart city improvement. But nowadays, the number of accidents on the road rises every year due to more vehicles on the road. Vehicle speed is only one reason for road accidents. So that traffic management authority needs a better surveillance system. Due to the significant expense of radar and less precision, the radar system is not able to become popular in the traffic surveillance system. And the inductive loops technique also needs high support. Therefore it is necessary to propose some mechanisms to overcome such problems. This paper proposed a novel computer vision-based automated system for multi-vehicle detection, tracking, and estimation of vehicle speed in a video sequence by using image processing. This proposed method contains several steps for vehicle speed estimation. In the first step, pre-processing is applied to extracted frames to reduce noise. And then, the gradient edge detection algorithm is used for moving vehicle edge extraction. The next step is template matching by using a normalized cross-correlation method. In this proposed system, detected vehicles tracked by using the vehicle's frame coordinates. Finally, speed is estimated using the number of frames and frame rate. The detection and tracking accuracy achieved by the proposed approach is about 97.33% and 97.2%. Overall precision, error rate, F1_score, sensitivity, kappa value is 98.19%, 0.0267, 97.43, 96.68% and 0.9466 respectively.

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Performance Analysis of Vehicle Detection Techniques: A Concise Survey

Cited by 6 publications

References 38 publications

Machine Learning Methods for Forest Image Analysis and Classification: A Survey of the State of the Art

Machine Learning Methods for Forest Image Analysis and Classification: A Survey of the State of the Art

Towards AI-Based Traffic Counting System with Edge Computing

Development of Vehicle Speed Estimation Technique using Image Processing

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