scholarly journals Comparative Analysis of Tracking Objects Using Optical Flow and Background Estimation on Silent Camera

2018 ◽  
pp. 153-162
Author(s):  
Wahyu Supriyatin ◽  
Winda Widya Ariestya ◽  
Ida Astuti
Keyword(s):  
Computer Vision ◽  
Optical Flow ◽  
Object Tracking ◽  
Estimation Method ◽  
Flow Estimation ◽  
Background Estimation ◽  
Existing Problems ◽  
Parameter Values ◽  
Tracking Objects ◽  
Better Than

Tracking and object is one of the utilizations on the field of the computer vision application. Object tracking utilization as a computer vision in this study is used to identify objects which exist within a frame and calculate the number of objects passing within a frame. The utilization of computer vision in various fields of application can be used to solve the existing problems. The method used in object tracking is by comparison between optical flow estimation method with background method. The test is conducted by using a still camera for both methods by making changes to the parameter values used as a reference. The results of the tests, conducted on the three video objects by comparing the two methods show a Total Recorded Time better than those of the background estimation method, being smaller than 100 seconds. Testing both methods successfully identifies the object tracking and calculates the number of passing cars.

scholarly journals Optical flow background estimation for real-time pan/tilt camera object tracking

Measurement ◽  
2014 ◽  
Vol 48 ◽  
pp. 195-207 ◽  
Author(s):  
Daniel D. Doyle ◽  
Alan L. Jennings ◽  
Jonathan T. Black
Keyword(s):  
Optical Flow ◽  
Object Tracking ◽  
Real Time ◽  
Background Estimation

A new optical flow estimation method in joint EO/IR video surveillance

2007 ◽  
Author(s):  
Hong Man ◽  
Robert J. Holt ◽  
Jing Wang ◽  
Rainer Martini ◽  
Ravi Netravali ◽  
...  
Keyword(s):  
Optical Flow ◽  
Video Surveillance ◽  
Estimation Method ◽  
Flow Estimation ◽  
Optical Flow Estimation

Quaternion based optical flow estimation for robust object tracking

2013 ◽  
Vol 23 (1) ◽  
pp. 118-125 ◽  
Author(s):  
Erkang Chen ◽  
Yi Xu ◽  
Xiaokang Yang ◽  
Wenjun Zhang
Keyword(s):  
Optical Flow ◽  
Object Tracking ◽  
Flow Estimation ◽  
Optical Flow Estimation

scholarly journals Object Tracking by Detection using YOLO and SORT

2020 ◽  
pp. 224-229
Author(s):  
Heet Thakkar ◽  
Noopur Tambe ◽  
Sanjana Thamke ◽  
Vaishali K. Gaidhane
Keyword(s):  
Computer Vision ◽  
Object Tracking ◽  
Visual Object ◽  
Visual Object Tracking ◽  
The Past ◽  
Video Frames ◽  
Detection Approach ◽  
Tracking By Detection ◽  
Sort Algorithm ◽  
Tracking Objects

Over the past two decades, computer vision has received a great deal of coverage. Visual object tracking is one of the most important areas of computer vision. Tracking objects is the process of tracking over time a moving object (or several objects). The purpose of visual object tracking in consecutive video frames is to detect or connect target objects. In this paper, we present analysis of tracking-by-detection approach which include detection by YOLO and tracking by SORT algorithm. This paper has information about custom image dataset being trained for 6 specific classes using YOLO and this model is being used in videos for tracking by SORT algorithm. Recognizing a vehicle or pedestrian in an ongoing video is helpful for traffic analysis. The goal of this paper is for analysis and knowledge of the domain.

scholarly journals Enhanced Real-Time Intermediate Flow Estimation for Video Frame Interpolation

2021 ◽  
Author(s):  
Minseop Kim ◽  
Haechul Choi
Keyword(s):  
Optical Flow ◽  
Real Time ◽  
Estimation Method ◽  
Frame Rate ◽  
Video Frame ◽  
High Quality ◽  
Frame Interpolation ◽  
Real Time Processing ◽  
High Frame Rate ◽  
Flow Estimation

Recently, the demand for high-quality video content has rapidly been increasing, led by the development of network technology and the growth in video streaming platforms. In particular, displays with a high refresh rate, such as 120 Hz, have become popular. However, the visual quality is only enhanced if the video stream is produced at the same high frame rate. For the high quality, conventional videos with a low frame rate should be converted into a high frame rate in real time. This paper introduces a bidirectional intermediate flow estimation method for real-time video frame interpolation. A bidirectional intermediate optical flow is directly estimated to predict an accurate intermediate frame. For real-time processing, multiple frames are interpolated with a single intermediate optical flow and parts of the network are implemented in 16-bit floating-point precision. Perceptual loss is also applied to improve the cognitive performance of the interpolated frames. The experimental results showed a high prediction accuracy of 35.54 dB on the Vimeo90K triplet benchmark dataset. The interpolation speed of 84 fps was achieved for 480p resolution.

scholarly journals RGBD Scene Flow Estimation with Global Nonrigid and Local Rigid Assumption

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Xiuxiu Li ◽  
Yanjuan Liu ◽  
Haiyan Jin ◽  
Lei Cai ◽  
Jiangbin Zheng
Keyword(s):  
Computer Vision ◽  
Estimation Method ◽  
Image Inpainting ◽  
Rigid Motion ◽  
Temporal Correlation ◽  
Depth Image ◽  
Depth Sensor ◽  
Flow Estimation ◽  
Scene Flow ◽  
Different Parts

RGBD scene flow has attracted increasing attention in the computer vision with the popularity of depth sensor. To estimate the 3D motion of object accurately, a RGBD scene flow estimation method with global nonrigid and local rigid motion assumption is proposed in this paper. Firstly, the preprocessing is implemented, which includes the colour-depth registration and depth image inpainting, to processing holes and noises in the depth image; secondly, the depth image is segmented to obtain different motion regions with different depth values; thirdly, scene flow is estimated based on the global nonrigid and local rigid assumption and spatial-temporal correlation of RGBD information. In the global nonrigid and local rigid assumption, each segmented region is divided into several blocks, and each block has a rigid motion. With this assumption, the interaction of motion from different parts in the same segmented region is avoided, especially the nonrigid object, e.g., a human body. Experiments are implemented on RGBD tracking dataset and deformable 3D reconstruction dataset. The visual comparison shows that the proposed method can distinguish the motion parts from the static parts in the same region better, and the quantitative comparisons proved more accurate scene flow can be obtained.

scholarly journals A multi-aperture optical flow estimation method for an artificial compound eye

2019 ◽  
Vol 26 (2) ◽  
pp. 139-157 ◽  
Author(s):  
Sidong Wu ◽  
Gexiang Zhang ◽  
Ferrante Neri ◽  
Ming Zhu ◽  
Tao Jiang ◽  
...  
Keyword(s):  
Optical Flow ◽  
Compound Eye ◽  
Estimation Method ◽  
Flow Estimation ◽  
Optical Flow Estimation

Object Tracking Based on Dual-View Stereo System

2013 ◽  
Vol 850-851 ◽  
pp. 780-783
Author(s):  
Jian De Fan ◽  
Jiang Bo Zhu
Keyword(s):  
Computer Vision ◽  
Optical Flow ◽  
Object Tracking ◽  
Moving Objects ◽  
Current Method ◽  
Research Area ◽  
Tracking Algorithm ◽  
Video Sequences ◽  
Feature Points ◽  
Motion Trajectories

Tracking moving objects in dual-view stereo system is becoming a hot research area in computer vision. To capture the moving objects pixels more accurately, we proposed a new object tracking algorithm which first compute moving objects feature points and then match these points, finally connect the matching feature points and get objects motion trajectories. The algorithm was tested in the video sequences with resolution 640×480 and 768×576 individually. The results show that the algorithm is more robust and the trajectories of the moving objects tracked with our method are more accurate compared with current method of L-K optical flow.

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