Automatic vehicle detection system in Day and Night Mode: challenges, applications and panoramic review

被引:8
作者
Arora, Nitika [1 ]
Kumar, Yogesh [2 ]
机构
[1] Chandigarh Engn Coll, Dept Comp Sci & Engn, Chandigarh, India
[2] Indus Univ, Ind Inst Technol Engn, Dept Comp Sci & Engn, Ahmadabad, Gujarat, India
关键词
Vehicle detection; Classification; Hypothesis generation; Intelligent Transport System; Hypothesis Verification; CLASSIFICATION; ROAD; TRACKING; LOOKING;
D O I
10.1007/s12065-022-00723-0
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Vehicle Detection and Recognition is a challenging move in the field of Traffic Management as it requires special attention and technique for the efficient management of vehicles. Vehicle Recognition and classification is a critical application of Intelligent Transport System (ITS). It is a process of identifying the moving vehicle on the road to analyze the flow rate and then accurately classify different objects. Lately, building an automatic onboard driver assistance system to assist drivers about possible collisions and clashes has received immense significance. Many researchers have proposed different methodologies using different source inputs to detect day and night vision vehicles. However, vehicle detection at night is an uphill task. It involves testing of classification algorithm under various factors such as Rainy weather, Snowy weather, Low illumination, etc., due to which identification of vehicle becomes a difficult task. This paper presents a comprehensive panorama of the work done so far by the researchers in vehicle detection day and night time. Various vehicle detection methods are discussed, along with the role of ITS in the application of vehicle detection and recognition. Also, it provides a concise review of the reported methods used for recognizing different types of vehicles in different environments and challenges faced by other researchers in their research area.
引用
收藏
页码:1077 / 1095
页数:19
相关论文
共 62 条
[1]  
[Anonymous], 2017, IEEE INT C COMPUTATI
[2]  
Aqel S, 2017, 2017 INTELLIGENT SYSTEMS AND COMPUTER VISION (ISCV)
[3]   A deep-learning-based computer vision solution for construction vehicle detection [J].
Arabi, Saeed ;
Haghighat, Arya ;
Sharma, Anuj .
COMPUTER-AIDED CIVIL AND INFRASTRUCTURE ENGINEERING, 2020, 35 (07) :753-767
[4]   Multimodal vehicle detection: fusing 3D-LIDAR and color camera data [J].
Asvadi, Alireza ;
Garrote, Luis ;
Premebida, Cristiano ;
Peixoto, Paulo ;
Nunes, Urbano J. .
PATTERN RECOGNITION LETTERS, 2018, 115 :20-29
[5]  
Baghdadi S., 2019, International Journal of Machine Learning and Computing, V9, P712, DOI 10.18178/ijmlc.2019.9.6.863
[6]  
Billones RKC, 2018, VEHICLE PEDESTRIAN C
[7]  
Charouh Z, 2019, IEEE INT S INNOVATIO
[8]   An algorithm for highway vehicle detection based on convolutional neural network [J].
Chen, Linkai ;
Ye, Feiyue ;
Ruan, Yaduan ;
Fan, Honghui ;
Chen, Qimei .
EURASIP JOURNAL ON IMAGE AND VIDEO PROCESSING, 2018,
[9]  
Chen X, 2014, 2 IAPR ASIAN C PATTE
[10]  
Chen ZZ, 2011, IEEE INT C INTELL TR, P74, DOI 10.1109/ITSC.2011.6083075