Road traffic detection and tracking with deep neural network
| dc.contributor.author | Wong, Kai Kit | |
| dc.date.accessioned | 2021-03-01T06:17:39Z | |
| dc.date.available | 2021-03-01T06:17:39Z | |
| dc.date.issued | 2019-06 | |
| dc.description.abstract | In Malaysia, road condition is getting more challenging especially in an urban area like Penang. There are more registered vehicles on the road and more traffic conflict happened. Traffic managers are required to monitor the traffic condition and analyse it for future road planning via road closed-circuit television (CCTV) camera. This monitoring system requires a lot of concentration and long working hours. Thus, human error is high. Hence, vision-based traffic surveillance system is used to assist traffic manager to process the input video frame from CCTVs to more meaningful data. This research’s aim is to build a deep neural network-based traffic surveillance system which is suitable for Penang traffic road condition. The proposed traffic surveillance system includes a vehicle detection algorithm by using DNN and vehicle tracking with a tracking-by-detection algorithm. The first stage is the vehicle detection algorithm. Three experiments have conducted which are YOLOv2 with pretrain COCO dataset, YOLOv3 with pretrain COCO dataset and YOLOv3 trained with custom dataset. The custom dataset combined MIO-TCD localization dataset and motorbike of Nepal vehicle detection dataset. YOLOv2 with pretrain COCO dataset performs poorly with input video frame which precision of truck and motorbike are 0.00%. YOLOv3 with pretrain COCO datasets perform well with car and bus class which have 72.97% and 92.00% of precision. However, the precision of truck and motorbike is just 46.23% and 40.37% respectively. At the same time, YOLOv3 with pretrain COCO dataset can detect small vehicles that cannot be detected with YOLOv2 model. For YOLOv3 that istrained with custom dataset, Motorbike class cannot be detected as the number of motorbike images is insufficient for training. Overall performance of other classes is good which precision of car, truck, and bus class are 92.05%, 64.71% and 70.77% respectively. From field test observation, the trained model does not have the problem that is faced with YOLOv3 with pretrain COCO dataset. For vehicle tracking algorithm, Euclidean distance is used to identify the same vehicle in the video frame with two important parameters which are assigned identity disappear parameter after number of frames and distance parameter to assume centroids of current frame and previous frame as the same identity. | en_US |
| dc.identifier.uri | http://hdl.handle.net/123456789/11621 | |
| dc.language.iso | en | en_US |
| dc.title | Road traffic detection and tracking with deep neural network | en_US |
| dc.type | Other | en_US |
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