Real Time Density Based Traffic Surveillance System Integrated with Acoustic Based Emergency Vehicle Detection

2020 ◽  
Author(s):  
Sai Surya Prakash Moka ◽  
Sai Manikanta Pilla ◽  
S. Radhika
Keyword(s):  
Real Time ◽  
Surveillance System ◽  
Vehicle Detection ◽  
Emergency Vehicle ◽  
Traffic Surveillance ◽  
Time Density

scholarly journals Distributed and collaborative real-time vehicle detection and classification over the video streams

2017 ◽  
Vol 14 (4) ◽  
pp. 172988141772078 ◽  
Author(s):  
Seda Kul ◽  
Süleyman Eken ◽  
Ahmet Sayar
Keyword(s):  
Information Systems ◽  
Real Time ◽  
Real World ◽  
Traffic Management ◽  
Streaming Media ◽  
Vehicle Detection ◽  
Experimental Results ◽  
Traffic Surveillance ◽  
Video Streams ◽  
Real World Applications

Traffic surveillance cameras are widely used in traffic management and information systems. Processing streaming media in real time is resource and time-consuming processes and even impossible to realize in most real-world applications. To overcome the performance problems in such applications, this article introduces a middleware system based on pub/sub messaging protocol and a dispatcher to preprocess the streams in real time. Experimental results show that proposed middleware may be utilized in different areas such as infrastructure planning, traffic management, and prevention of traffic offenses.

scholarly journals Vehicle Detection in Urban Traffic Surveillance Images Based on Convolutional Neural Networks with Feature Concatenation

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 594 ◽  
Author(s):  
Fukai Zhang ◽  
Ce Li ◽  
Feng Yang
Keyword(s):  
Real Time ◽  
Sequence Data ◽  
Vehicle Detection ◽  
Urban Traffic ◽  
Single Shot ◽  
Traffic Surveillance ◽  
Accurate Localization ◽  
Input Size ◽  
Comparable Accuracy ◽  
Feature Pyramid

Vehicle detection with category inference on video sequence data is an important but challenging task for urban traffic surveillance. The difficulty of this task lies in the fact that it requires accurate localization of relatively small vehicles in complex scenes and expects real-time detection. In this paper, we present a vehicle detection framework that improves the performance of the conventional Single Shot MultiBox Detector (SSD), which effectively detects different types of vehicles in real-time. Our approach, which proposes the use of different feature extractors for localization and classification tasks in a single network, and to enhance these two feature extractors through deconvolution (D) and pooling (P) between layers in the feature pyramid, is denoted as DP-SSD. In addition, we extend the scope of the default box by adjusting its scale so that smaller default boxes can be exploited to guide DP-SSD training. Experimental results on the UA-DETRAC and KITTI datasets demonstrate that DP-SSD can achieve efficient vehicle detection for real-world traffic surveillance data in real-time. For the UA-DETRAC test set trained with UA-DETRAC trainval set, DP-SSD with the input size of 300 × 300 achieves 75.43% mAP (mean average precision) at the speed of 50.47 FPS (frames per second), and the framework with a 512 × 512 sized input reaches 77.94% mAP at 25.12 FPS using an NVIDIA GeForce GTX 1080Ti GPU. The DP-SSD shows comparable accuracy, which is better than those of the compared state-of-the-art models, except for YOLOv3.

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