Vehicle detection and localization Pei-Hen Hung Media IC & System Lab

Intelligent Transportation System Benefits of M2M in ITS Expansion of sensor coverage/Increase of time allowed to react Goal: Develop a proactive driver assistance system Driver behavior modeling and inference M2M-based neighbor map building

Sensor Fusion

Visual Analysis Subsystem Development A Complete Vehicle Detection on RSU Background subtraction Feature extraction Grand data training and classifier Map calculation Detection in challenging conditions Distributed Video Benchmarking Preparation Device collection and testing Campus testing Ground truth testing/Calibration /Timing synchronization On-road testing Grand data collection

Methodology

Methodology Background subtraction using Gaussian of Mixture Model (GMM)

Histograms of edge orientations Methodology Histogram of Orientated Gradient (HoG ) Feature Extraction 8*8 cell size Histograms of edge orientations edge [-1, 0, 1] gradient filter with no smoothing 8*16 cells 9 unsighted bins=> 9 dimension vector

Methodology Training Stage Testing Stage Labeling target objects and extract features SVM training Testing Stage SVM testing

Data collection 長興街&芳蘭路口 4 Camera 1 Lidar 3 Scooter with GPS 1 Scooter with Lidar and GPS

RSU RSU Car RSU RSU Robot Car Scooter Car RSU RSU Robot Scooter Car

Demo

Event Object Signal Future work Two men walked in the office… Objects in the Video Conventional Scalability I(x,y,t)

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