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MultEYE: Real-time Vehicle Detection and Speed Estimation from Aerial Images using Multi-Task Learning

Balamuralidhar, Navaneeth (2020) MultEYE: Real-time Vehicle Detection and Speed Estimation from Aerial Images using Multi-Task Learning.

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Abstract:Though traffic monitoring systems, in recent years, have seen automation incorporated into its infrastructure, the area under the scope of surveillance is still small. The per square kilometer investment required deters authorities from large-scale deployment plans. A UAV mounted surveillance solution can address this issue at a fraction of the cost. During the course of this research, an end-to-end system that can detect vehicles from aerial image sequences and estimate their speed in real-time was built. The system consists of three parts: Vehicle Detector, Vehicle Tracker, and Speed Estimator. The vehicle detector uses the concept of multi-task learning to learn object detection and semantic segmentation simultaneously on an architecture custom-designed for vehicle detection called MultEYE, which achieves 1.2% higher mAP score while being 91.4% faster than the state-of-the-art model on a custom dataset. An extremely fast algorithm called MOSSE, which runs multi-object tracking at around 300FPS, serves as the vehicle tracker for the system. Speeds of the tracked vehicles are estimated using a combination of optical flow for motion compensation and known estimates of vehicle sizes as reference for scale. Further, the complete system’s performance is also optimized and benchmarked on an NVIDIA Jetson Xavier NX embedded computer to prove its deployability on mobile platforms capable of running on UAVs. The optimized system runs at an average frame-rate of up to 33.44 FPS on frame resolution 3072×1728 on the embedded platform.
Item Type:Essay (Master)
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:55 traffic technology, transport technology
Programme:Systems and Control MSc (60359)
Link to this item:http://purl.utwente.nl/essays/84758
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