Name
Title | ||
|---|---|---|
Tpcam: Real-Time Traffic Pattern Collection And Analysis Model Based On Deep Learning |
Abstract | ||
|---|---|---|
Real-time, robust and reliable traffic surveillance is one of the important requirements to improve urban traffic control systems and eliminate congestions. One of the major drawbacks with the existing traffic surveillance infrastructure is the need for storing and transmitting the huge video data before processing it. In this paper, we focus to enable the intelligence of traffic system as well as eliminate the need for storing and transmitting big data. To achieve this, we propose a real-time system called traffic pattern collection and analysis model (TPCAM) that aims at collecting and generating the statistics of traffic data at intersections. The system employs deep learning models and realtime algorithms to process live traffic information on a resource constrained embedded platform. However, the achievable frame rate on the most advanced embedded systems (NVIDIA Jetson TX2) is only 3 FPS, which is too low to capture the full moving trace of the vehicles. To improve the frame rate, we further propose deep object tracking algorithm leveraging adaptive multi modal models and make it robust to object occlusions and varying lighting conditions. Based on the deep learning based detection and tracking, we can achieve pseudo-30FPS via adaptive key frame selection. |
Year | Venue | Keywords |
|---|---|---|
2017 | 2017 IEEE SMARTWORLD, UBIQUITOUS INTELLIGENCE & COMPUTING, ADVANCED & TRUSTED COMPUTED, SCALABLE COMPUTING & COMMUNICATIONS, CLOUD & BIG DATA COMPUTING, INTERNET OF PEOPLE AND SMART CITY INNOVATION (SMARTWORLD/SCALCOM/UIC/ATC/CBDCOM/IOP/SCI) | traffic surveillance, real-time traffic data, edge computing, deep learning, multiple object tracking |
Field | DocType | Citations |
Airfield traffic pattern,Computer science,Video tracking,Urban traffic control,Artificial intelligence,Frame rate,Deep learning,Key frame,Big data,Traffic system,Distributed computing | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Unnikrishnan Kizhakkemadam Sreekumar | 1 | 0 | 0.68 |
| Revathy Devaraj | 2 | 0 | 1.01 |
| Qi Li | 3 | 410 | 78.59 |
| Kaikai Liu | 4 | 190 | 20.37 |