Cyclist Detection, Tracking, and Trajectory Analysis in Urban Traffic Video Data

The major objective of this thesis work is examining computer vision and machine learning detection methods, tracking algorithms and trajectory analysis for cyclists in traffic video data and developing an efficient system for cyclist counting. Due to the growing number of cyclist accidents on urban roads, methods for collecting information on cyclists are of significant importance to the Department of Transportation. The collected information provides insights into solving critical problems related to transportation planning, implementing safety countermeasures, and managing traffic flow efficiently. Intelligent Transportation System (ITS) employs automated tools to collect traffic information from traffic video data. In comparison to other road users, such as cars and pedestrians, the automated cyclist data collection is relatively a new research area. In this work, a vision-based method for gathering cyclist count data at intersections and road segments is developed. First, we develop methodology for an efficient detection and tracking of cyclists. The combination of classification features along with motion based properties are evaluated to detect cyclists in the test video data. A Convolutional Neural Network (CNN) based detector called You Only Look Once (YOLO) is implemented to increase the detection accuracy. In the next step, the detection results are fed into a tracker which is implemented based on the Kernelized Correlation Filters (KCF) which in cooperation with the bipartite graph matching algorithm allows to track multiple cyclists, concurrently. Then, a trajectory rebuilding method and a trajectory comparison model are applied to refine the accuracy of tracking and counting. The trajectory comparison is performed based on semantic similarity approach. The proposed counting method is the first cyclist counting method that has the ability to count cyclists under different movement patterns. The trajectory data obtained can be further utilized for cyclist behavioral modeling and safety analysis

Multiple Road Users Detection And Tracking System In Urban Mixed Traffic Scenes

Video analytic technology in traffic control and monitoring is getting more attention in recent years. This is because video analytic technology can perform traffic surveillance to extract traffic information such as vehicle counting and classification from video sequence. Large amount of road user data can be generated from video and this data would benefit for traffic planner. This provides the impact of video analytic in traffic surveillance. However, multiple road users tracking in urban traffic remains challenging because of large variation of road user appearance. To overcome the problems of multiple object tracking in mixed urban traffic, which are mis-detection, frequent ID switches and mis-classification, a system known as City Tracker, which incorporates Maximum Likelihood Estimation (MLE), YOLOv3 and DeepSORT is proposed in mixed urban traffic. City Tracker predicts the potential bounding box coordinates from the result of YOLOv3 and DeepSORT, then matches with the latest actual bounding box to overcome the mis-detection and frequent identity switch. On the other hand, MLE provides trajectory-based classification to solve mis-classification. This solution is tested with Urban Tracker dataset based on detection and tracking performance. The performance evaluations show that implementation of City Tracker increases Multiple Object Tracking Accuracy (MOTA) from 0.3503 to 0.3793 (8.28%) and Multiple Object Tracking Precision (MOTP) from 0.6245 to 0.6442 (3.15%) which are calculated from Precision and Recall as the evaluation metrics. MLE improves Recall from 0.7032 to 0.7838 (11.46%) and Precision from 0.7214 to 0.8334 (15.53%) in classification performance, which is better than conventional YOLOv3 and DeepSORT that do not consider City Tracker

Identificació no-supervisada de persones en programes de televisió

En els darrers anys la quantitat de dades de vídeos i imatges ha anat augmentant, això ha provocat diversos problemes d’anotació i classificació del conjunt de dades. Un d’aquests grans problemes és la identificació de persones en vídeos, és per això que la recerca en aquest àmbit ha incrementat en els últims anys. L’objectiu d’aquest projecte és trobar un nou algoritme per poder millorar la identificació no supervisada de persones en seqüències de vídeo per programes de televisió. Per poder dur a terme aquesta millora es crearà un nou classificador des de cero per poder identificar si una persona en una seqüència de vídeo està parlant o no. Aquest classificador serà creat a partir de l’extracció de les cares de persones en diferents vídeos, i classificant-les manualment respecte si estan parlant o no. A partir d’aquest conjunt de dades, es detectaran les boques i es mesurarà la distància entre els llavis, per tal de poder crear un vector de distàncies per cada cara detectada. A més a més, s’aplicarà un detector facial millorat i es compararan els resultats amb un altre detector més antic. Finalment s’exposaran els resultats un cop aplicat el nou classificado