Abstract
Smart traffic management is being proposed for better management of traffic infrastructure and regulate traffic in smart cities. With surge of traffic density in many cities, smart traffic management becomes utmost necessity. Vehicle categorization, traffic density estimation and vehicle tracking are some of the important functionalities in smart traffic management. Vehicles must be categorized based on multiple levels like type, speed, direction of travel and vehicle attributes like color etc. for efficient tracking and traffic density estimation. Vehicle categorization becomes very challenging due to occlusions, cluttered backgrounds and traffic density variations. In this work, a traffic adaptive multi-level vehicle categorization using deep learning is proposed. The solution is designed to solve the problems in vehicle categorization in terms of occlusions, cluttered backgrounds.
Highlights
Smart traffic management based on video feeds from traffic surveillance cameras is being proposed as a means for efficient traffic regulation at a lower cost compared to sensors based traffic management.Smart traffic management aims to regulate the traffic conditions in peak hours, manage congestions, transport of emergency vehicles, detect and handle accidents/incidents in the road
This work deals with this need and proposes a traffic adaptive deep learning multi-level vehicle categorization which can work in conditions of cluttered background and occlusions in the video
A traffic adaptive fine grained vehicular classification using deep learning is proposed in this work
Summary
Smart traffic management based on video feeds from traffic surveillance cameras is being proposed as a means for efficient traffic regulation at a lower cost compared to sensors based traffic management. Smart traffic management aims to regulate the traffic conditions in peak hours, manage congestions, transport of emergency vehicles, detect and handle accidents/incidents in the road. Vehicle categorization is an important functionality in smart traffic management. Is important for localization and tracking of vehicles in smart traffic management. The problem becomes even more difficult in case of need for fine grained multi-level categorization like learning speed, direction and meta attributes of the vehicles from the video stream. This work deals with this need and proposes a traffic adaptive deep learning multi-level vehicle categorization which can work in conditions of cluttered background and occlusions in the video. A deep learning optimized topological active net segmentation is done to segment the vehicles in case of low density traffic. Features are extracted and mapping is done to learn various meta-attributes of the segmented vehicles
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