Abstract
Speed detection of a moving object using an optical camera has always been an important subject to study in computer vision. This is one of the key components to address in many application areas, such as transportation systems, military and naval applications, and robotics. In this study, we implemented a speed detection system for multiple moving objects on the ground from a moving platform in the air. A detect-and-track approach is used for primary tracking of the objects. Faster R-CNN (region-based convolutional neural network) is applied to detect the objects, and a discriminative correlation filter with CSRT (channel and spatial reliability tracking) is used for tracking. Feature-based image alignment (FBIA) is done for each frame to get the proper object location. In addition, SSIM (structural similarity index measurement) is performed to check how similar the current frame is with respect to the object detection frame. This measurement is necessary because the platform is moving, and new objects may be captured in a new frame. We achieved a speed accuracy of 96.80% with our framework with respect to the real speed of the objects.
Highlights
Real-time traffic monitoring is a challenging task
New technologies are introduced into this field to make intelligent traffic monitoring systems (ITMS) better
Faster R-convolutional neural networks (CNN) is used for vehicle detection in this research
Summary
Real-time traffic monitoring is a challenging task. Even with all the best technologies we have, we are still struggling with insufficient information on the road to solve traffic problems. New technologies are introduced into this field to make intelligent traffic monitoring systems (ITMS) better. One of the latest technological additions to this field is the use of UAVs (unmanned aerial vehicles). UAVs could be very effective in tracking and monitoring a vehicle for law enforcement and crime prevention. The short battery lives of the UAVs and a lack of efficient algorithms to detect and track moving vehicles [5] are the main factors preventing this technology from being widely used. We estimated the speed of multiple vehicles from a moving UAV platform using video streams of optical cameras. Tracking moving vehicles from a UAV platform will be extremely effective if the speed of the moving vehicles can be accurately estimated.
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