Situation-Based Dynamic Frame-Rate Control for on-Line Object Tracking

2018 ◽  
Author(s):  
Yusuke Inoue ◽  
Takatsuau Ono ◽  
Koji Inouer
Keyword(s):  
Object Tracking ◽  
Rate Control ◽  
Frame Rate ◽  
On Line

A region-based intra-frame rate control scheme by jointing inter-frame dependency and inter-frame correlation

2016 ◽  
Vol 76 (10) ◽  
pp. 12917-12940
Author(s):  
Hai-Miao Hu ◽  
Mingliang Zhou ◽  
Yang Liu ◽  
Naiyu Yin
Keyword(s):  
Rate Control ◽  
Frame Rate ◽  
Control Scheme ◽  
Inter Frame

scholarly journals Reconfigurable fuzzy logic system for high-frame rate stereovision object tracking

2021 ◽  
Author(s):  
Oleg Samarin
Keyword(s):  
Fuzzy Logic ◽  
Control System ◽  
Object Tracking ◽  
Fuzzy Logic Control ◽  
Tracking System ◽  
Frame Rate ◽  
Disparity Map ◽  
High Frame Rate ◽  
Logic Control ◽  
Logic System

his study investigates the applicability of fuzzy logic control to high-frame rate stereovision object tracking. The technology developed in this work is based on utilizing a disparity map produced by the Stereovision Tracking System (STS) to identify the object of interest. The coordinates of the object are used by the fuzzy logic control system to provide rotation and focus control for object tracking. The fuzzy logic control was realized as a reconfigurable hardware module and implemented on Virtex-2 FPGA platform of the STS. The fuzzy reasoning was implemented as a reconfigurable look-up table residing in FPGA's internal memory. A set of software tools facilitating creation of loop-up table and reconfiguration of fuzzy logic control system was developed. Finally, the experimental prototype of the system was built and tested.

Joint RD Model for H.264/AVC Intra frame Rate Control

2012 ◽  
Vol 6 (15) ◽  
pp. 510-517
Author(s):  
Lihua Zheng ◽  
Ling Tian ◽  
Jianghong Zhang
Keyword(s):  
Rate Control ◽  
Frame Rate

scholarly journals Multiple-Object-Tracking Algorithm Based on Dense Trajectory Voting in Aerial Videos

2019 ◽  
Vol 11 (19) ◽  
pp. 2278
Author(s):  
Tao Yang ◽  
Dongdong Li ◽  
Yi Bai ◽  
Fangbing Zhang ◽  
Sen Li ◽  
...  
Keyword(s):  
Optical Flow ◽  
Object Tracking ◽  
Target Tracking ◽  
Multiple Object Tracking ◽  
Tracking Algorithm ◽  
Frame Rate ◽  
Dense Optical Flow ◽  
Multiple Object ◽  
Dense Trajectory ◽  
Low Frame Rate

In recent years, UAV technology has developed rapidly. Due to the mobility, low cost, and variable monitoring altitude of UAVs, multiple-object detection and tracking in aerial videos has become a research hotspot in the field of computer vision. However, due to camera motion, small target size, target adhesion, and unpredictable target motion, it is still difficult to detect and track targets of interest in aerial videos, especially in the case of a low frame rate where the target position changes too much. In this paper, we propose a multiple-object-tracking algorithm based on dense-trajectory voting in aerial videos. The method models the multiple-target-tracking problem as a voting problem of the dense-optical-flow trajectory to the target ID, which can be applied to aerial-surveillance scenes and is robust to low-frame-rate videos. More specifically, we first built an aerial video dataset for vehicle targets, including a training dataset and a diverse test dataset. Based on this, we trained the neural network model by using a deep-learning method to detect vehicles in aerial videos. Thereafter, we calculated the dense optical flow in adjacent frames, and generated effective dense-optical-flow trajectories in each detection bounding box at the current time. When target IDs of optical-flow trajectories are known, the voting results of the optical-flow trajectories in each detection bounding box are counted. Finally, similarity between detection objects in adjacent frames was measured based on the voting results, and tracking results were obtained by data association. In order to evaluate the performance of this algorithm, we conducted experiments on self-built test datasets. A large number of experimental results showed that the proposed algorithm could obtain good target-tracking results in various complex scenarios, and performance was still robust at a low frame rate by changing the video frame rate. In addition, we carried out qualitative and quantitative comparison experiments between the algorithm and three state-of-the-art tracking algorithms, which further proved that this algorithm could not only obtain good tracking results in aerial videos with a normal frame rate, but also had excellent performance under low-frame-rate conditions.

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