Obstacle detection and classification using deep learning for tracking in high-speed autonomous driving

TENSYMP 2017 - IEEE International Symposium on Technologies for Smart Cities

Volumn , Issue , 2017, Pages

  Prabhakar, Gowdham ^a   Kailath, Binsu ^a   Natarajan, Sudha ^b   Kumar, Rajesh ^b  

^a INDIAN INSTITUTE OF INFORMATION TECHNOLOGY   (India)

^b ETSI SOL006 Rapporteur   (India)

Author keywords

Autonomous driving; convolutional neural network; deep learning; object classification; object detection; R CNN

Indexed keywords

AUTOMOBILE FRAMES; CONVOLUTION; COST EFFECTIVENESS; DEEP LEARNING; GRAPHICS PROCESSING UNIT; NEURAL NETWORKS; OBJECT DETECTION; OBSTACLE DETECTORS; ROADS AND STREETS; SMART CITY; TRANSPORTATION; VEHICLES;

AUTONOMOUS DRIVING; CLASSIFICATION RESULTS; CLIMATIC CONDITIONS; CONVOLUTIONAL NEURAL NETWORK; OBJECT CLASSIFICATION; PERCEPTION SYSTEMS; R-CNN; VISION-BASED APPROACHES;

CLASSIFICATION (OF INFORMATION);

EID: 85040010390     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/TENCONSpring.2017.8069972     Document Type: Conference Paper

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