Vision-based Android Application for GPS Assistance in Tunnels The Andrew & Erna Viterbi Faculty of Electrical Engineering Electronics Computers Communications Vision-based Android Application for GPS Assistance in Tunnels Lior Carmi, Shirin Manzur, Supervisor: Amit Aides The Problem GPS navigation with smartphone (‘WAZE’) Signal loss in tunnels Late or missing navigation directions Test case: Carmel Tunnels Road Sign Recognition Rectangle Recognition 1 2 3 .. 12 other SVM Classifier ? Final Algorithm Initial velocity & location Road Mark Recognition Kalman Filter Suggested Solution Utilize camera to estimate location Feature point tracking: Tested using PTAM algorithm Fails due to fast changing video frames Tunnel light tracking: Easy to recognize / no occlusions / known distances Recognition of known infrastructure: Road signs every 250 meter Evaluate usefulness of motion sensors: Useful for small distances only Estimated speed and location Absolute location Speed Samples (from Light Recognition) 0.1 sec Last location Last velocity Kalman Filter State System Update Prediction x(k+1|k) x(k+1) x(k) z(k+1) Current state Predict next state Estimate next state Measurement Workflow Exploration of existing solutions Computer simulations Algorithm development Real-time implementation Application development PTAM Results Tunnel Light Tracking Results ROI & Convert to binary (dynamic threshold) Real-time Android Application Computer Simulation Estimation Error at tunnel exit -- 15 m ( < 0.5% error) Both methods * 45 m ( < 2% error) 30 m ( < 1% error) Light Tracking only Shape based filtering, (morphological operations) Classification of objects (linear series of 3 lights) * Both methods = Light tracking & Road sign recognition Summery Real-time Android application Successfully tested on test-case Easy adjustment to any tunnel Easy integration with WAZE or similar Demo Detect new light (10% of top of frame) Calculate speed LampsDelta=17 [m] FPS=10 [frames/sec] Distinguished Project Award in EE Project Contest June 2015