Vsion: Vehicle occlusion handling for traffic monitoring

• Main Authors: Castillo, Reneé Dominique M., Tejada, Mima Maiden B., Cordel, Macario O., Laguna, Ann Franchesca B., Ilao, Joel P.
• Format: text
• Published: Animo Repository 2017
• Subjects: Vehicles—Classification; Vehicle detectors; Neural networks (Computer science); Image processing; Traffic monitoring; Computer Engineering; Computer Sciences
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/3018
• Institution: De La Salle University

Recently, the pervasiveness of street cameras for security and traffic monitoring opens new challenges to the computer vision technology to provide reliable monitoring schemes. These monitoring schemes require the basic processes of detecting and tracking objects, such as vehicles. However, object detection performance often suffers under occlusion. This work proposes a vehicle occlusion handling improvement of an existing traffic video monitoring system, which was later integrated. Two scenarios were considered in occlusion: indistinct and distinct - wherein the occluded vehicles have similar and dissimilar colors, respectively. K-means clustering using the HSV color space was used for distinct occlusion while sliding window algorithm was used for indistinct occlusion. The proposed method also applies deep convolutional neural networks to further improve vehicle recognition and classification. The CNN model obtained a 97.21% training accuracy and a 98.27% testing accuracy. Moreover, it minimizes the effect of occlusion to vehicle detection and classification. It also identifies common vehicle types (bus, truck, van, sedan, SUV, jeepney, and motorcycle) rather than classifying these as small, medium and large vehicles, which were the previous categories. Despite the implementation and results, it is recommended to improve the occlusion handling issue. The disadvantage of the sliding window algorithm is that it requires a lot of memory and is time-consuming. In case of deploying this research for more substantial purposes and intentions, it is ideal to enhance the CNN model by training it with more varied images of vehicles and to implement the system real-time. The results of this work can serve as a contribution for future works that are significant to traffic monitoring and air quality surveillance. © 2017 Association for Computing Machinery.