scholarly journals Asynchronous Semantic Background Subtraction

2020 ◽  
Vol 6 (6) ◽  
pp. 50
Author(s):  
Anthony Cioppa ◽  
Marc Braham ◽  
Marc Van Droogenbroeck
Keyword(s):  
Real Time ◽  
Background Subtraction ◽  
Semantic Information ◽  
Moving Objects ◽  
Temporal Evolution ◽  
Semantic Segmentation ◽  
Feedback Mechanism ◽  
Segmentation Algorithm ◽  
Frame Rate ◽  
The One

The method of Semantic Background Subtraction (SBS), which combines semantic segmentation and background subtraction, has recently emerged for the task of segmenting moving objects in video sequences. While SBS has been shown to improve background subtraction, a major difficulty is that it combines two streams generated at different frame rates. This results in SBS operating at the slowest frame rate of the two streams, usually being the one of the semantic segmentation algorithm. We present a method, referred to as “Asynchronous Semantic Background Subtraction” (ASBS), able to combine a semantic segmentation algorithm with any background subtraction algorithm asynchronously. It achieves performances close to that of SBS while operating at the fastest possible frame rate, being the one of the background subtraction algorithm. Our method consists in analyzing the temporal evolution of pixel features to possibly replicate the decisions previously enforced by semantics when no semantic information is computed. We showcase ASBS with several background subtraction algorithms and also add a feedback mechanism that feeds the background model of the background subtraction algorithm to upgrade its updating strategy and, consequently, enhance the decision. Experiments show that we systematically improve the performance, even when the semantic stream has a much slower frame rate than the frame rate of the background subtraction algorithm. In addition, we establish that, with the help of ASBS, a real-time background subtraction algorithm, such as ViBe, stays real time and competes with some of the best non-real-time unsupervised background subtraction algorithms such as SuBSENSE.

Real-Time Semantic Segmentation Algorithm Based on Feature Fusion Technology

2020 ◽  
Vol 57 (2) ◽  
pp. 021011
Author(s):  
蔡雨 Cai Yu ◽  
黄学功 Huang Xuegong ◽  
张志安 Zhang Zhian ◽  
朱新年 Zhu Xinnian ◽  
马祥 Ma Xiang
Keyword(s):  
Real Time ◽  
Feature Fusion ◽  
Semantic Segmentation ◽  
Segmentation Algorithm ◽  
Fusion Technology

scholarly journals Real Time Tracking RGB Color Based Kinect

2017 ◽  
Vol 11 (3) ◽  
pp. 98
Author(s):  
Ahmed Mustafa Taha Alzbier ◽  
Hang Cheng
Keyword(s):  
Computer Vision ◽  
Real Time ◽  
Video Processing ◽  
Background Subtraction ◽  
Moving Objects ◽  
Blue Color ◽  
The Real ◽  
Color Recognition ◽  
Kinect Camera ◽  
Computer Vision Technology

As the present computer vision technology is growing up, and the multiple RGB color object tracking is considered as one of the important tasks in computer vision and technique that can be used in many applications such as surveillance in a factory production line, event organization, flow control application, analysis and sort by colors and etc. In video processing applications, variants of the background subtraction method are broadly used for the detection of moving objects in video sequences. The background subtraction is the most popular and common approach for motion detection. However , this is paper presents our investigation the first objective of the whole algorithm chain is to find the RGB color within a video. The idea from the beginning was to look for certain specific features of the patches, which would allow distinguishing red, green and blue color objects in the image. In this paper an algorithm is proposed to track the real time moving RGB color objects using kinect camera. We will use a kinect camera to capture the real time video and making an image frame from this video and extracting red, green and blue color .Here image processing is done through MATLAB for color recognition process each color. Our method can tracking accurately at 95% in real-time.

scholarly journals An Efficient Real Time Object Tracking Based on HSV Value

2021 ◽  
Vol 9 (11) ◽  
pp. 1197-1203
Author(s):  
Sheikh Summerah
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
Moving Objects ◽  
Color Space ◽  
Video Tracking ◽  
Frame Rate ◽  
Long Distance ◽  
Hsv Color Space ◽  
Video Frames ◽  
Testing Stage

Abstract: This study presents a strategy to automate the process to recognize and track objects using color and motion. Video Tracking is the approach to detect a moving item using a camera across the long distance. The basic goal of video tracking is in successive video frames to link target objects. When objects move quicker in proportion to frame rate, the connection might be particularly difficult. This work develops a method to follow moving objects in real-time utilizing HSV color space values and OpenCV in distinct video frames.. We start by deriving the HSV value of an object to be tracked and then in the testing stage, track the object. It was seen that the objects were tracked with 90% accuracy. Keywords: HSV, OpenCV, Object tracking,

scholarly journals Automatic Vehicles Detection, Classification and Counting Techniques / Survey

2020 ◽  
pp. 1811-1822
Author(s):  
Mustafa Najm ◽  
Yossra Hussein Ali
Keyword(s):  
Real Time ◽  
Intelligent Transportation Systems ◽  
Background Subtraction ◽  
Moving Objects ◽  
Vehicle Detection ◽  
Intelligent Transportation ◽  
Traffic Monitoring ◽  
Transportation Systems ◽  
Traffic Condition ◽  
Real Time Processing

Vehicle detection (VD) plays a very essential role in Intelligent Transportation Systems (ITS) that have been intensively studied within the past years. The need for intelligent facilities expanded because the total number of vehicles is increasing rapidly in urban zones. Traffic monitoring is an important element in the intelligent transportation system, which involves the detection, classification, tracking, and counting of vehicles. One of the key advantages of traffic video detection is that it provides traffic supervisors with the means to decrease congestion and improve highway planning. Vehicle detection in videos combines image processing in real-time with computerized pattern recognition in flexible stages. The real-time processing is very critical to keep the appropriate functionality of automated or continuously working systems. VD in road traffics has numerous applications in the transportation engineering field. In this review, different automated VD systems have been surveyed,  with a focus on systems where the rectilinear stationary camera is positioned above intersections in the road rather than being mounted on the vehicle. Generally, three steps are utilized to acquire traffic condition information, including background subtraction (BS), vehicle detection and vehicle counting. First, we illustrate the concept of vehicle detection and discuss background subtraction for acquiring only moving objects. Then a variety of algorithms and techniques developed to detect vehicles are discussed beside illustrating their advantages and limitations. Finally, some limitations shared between the systems are demonstrated, such as the definition of ROI, focusing on only one aspect of detection, and the variation of accuracy with quality of videos. At the point when one can detect and classify vehicles, then it is probable to more improve the flow of the traffic and even give enormous information that can be valuable for many applications in the future.

scholarly journals Object Tracking Using HSV Values and OpenCV

2021 ◽  
Vol 9 (12) ◽  
pp. 594-599
Author(s):  
Gowher Shafi
Keyword(s):  
Object Tracking ◽  
Real Time ◽  
Moving Objects ◽  
Video Tracking ◽  
Frame Rate ◽  
Colour Space ◽  
Long Distance ◽  
Video Frames ◽  
Hsv Colour Space ◽  
Percent Accuracy

Abstract: This research shows how to use colour and movement to automate the process of recognising and tracking things. Video tracking is a technique for detecting a moving object over a long distance using a camera. The main purpose of video tracking is to connect target objects in subsequent video frames. The connection may be particularly troublesome when things move faster than the frame rate. Using HSV colour space values and OpenCV in different video frames, this study proposes a way to track moving objects in real-time. We begin by calculating the HSV value of an item to be monitored, and then we track the object throughout the testing step. The items were shown to be tracked with 90 percent accuracy. Keywords: HSV, OpenCV, Object tracking, Video frames, GUI

scholarly journals Reconstruction of High-Precision Semantic Map

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6264
Author(s):  
Xinyuan Tu ◽  
Jian Zhang ◽  
Runhao Luo ◽  
Kai Wang ◽  
Qingji Zeng ◽  
...  
Keyword(s):  
Real Time ◽  
Point Cloud ◽  
Semantic Information ◽  
Feature Fusion ◽  
Three Dimensional ◽  
Semantic Segmentation ◽  
Line Of Sight ◽  
Lidar Data ◽  
Implicit Surface ◽  
Robotic Mapping

We present a real-time Truncated Signed Distance Field (TSDF)-based three-dimensional (3D) semantic reconstruction for LiDAR point cloud, which achieves incremental surface reconstruction and highly accurate semantic segmentation. The high-precise 3D semantic reconstruction in real time on LiDAR data is important but challenging. Lighting Detection and Ranging (LiDAR) data with high accuracy is massive for 3D reconstruction. We so propose a line-of-sight algorithm to update implicit surface incrementally. Meanwhile, in order to use more semantic information effectively, an online attention-based spatial and temporal feature fusion method is proposed, which is well integrated into the reconstruction system. We implement parallel computation in the reconstruction and semantic fusion process, which achieves real-time performance. We demonstrate our approach on the CARLA dataset, Apollo dataset, and our dataset. When compared with the state-of-art mapping methods, our method has a great advantage in terms of both quality and speed, which meets the needs of robotic mapping and navigation.

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