Object Tracking in a Zone using DeepSORT, YOLOv4 and TensorFlow

Jindal, Rajni; Panwar, Aditya; Sharma, Namita; Rai, Aman

doi:10.1109/incet51464.2021.9456443

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…YOLOv4 is used despite the existence of YOLOv5 because of better performance characteristics and relatively lower training time. Whereas DeepSORT is chosen because it has an average Multiple Object Tracking Accuracy (MOTA) of 68.7% and provides a better detection and tracking speed [9,10,11,12].…”

Section: Introductionmentioning

confidence: 99%

Counting Various Vehicles using YOLOv4 and DeepSORT

Kusumah,

Djayusman,

Setiadi

et al. 2023

JIAE

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The Ministry of Public Works and Public Housing (PUPR) conducted a traffic survey to determine the total number of vehicles and classify them according to the Bina Marga vehicle categorisation. The survey has thus far been carried out manually. As a result, surveys take a lot of time and money to perform. Additionally, as the survey scope grows, so will the requirement for surveyors. Therefore, a substitute that can execute the survey procedure automatically and with tolerable accuracy is required. One solution is to utilise deep learning technology to detect and categorise vehicles that can be used in apps. The program is designed as a web application that provides a summary of vehicle calculations and receives video data from traffic recordings. The deep learning model used is YOLOv4 which is trained to recognise vehicle classes following Bina Marga vehicle types. The model was trained and tested using the Python programming language and the Darknet framework on the Google Colab platform. The YOLOv4 and DeepSORT method with custom dataset reached a decent accuracy of 67.94%, considering the limited 1000 images used for training the model.

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Section: Introductionmentioning

confidence: 99%