scholarly journals Robust Visual Tracking Using the Bidirectional Scale Estimation

2017 ◽  
Vol 2017 ◽  
pp. 1-10
Author(s):  
An Zhiyong ◽  
Guan Hao ◽  
Li Jinjiang
Keyword(s):  
Computer Vision ◽  
Object Tracking ◽  
Visual Tracking ◽  
Ridge Regression ◽  
State Of The Art ◽  
Benchmark Dataset ◽  
Tracking Algorithm ◽  
Conservative Strategy ◽  
Scale Estimation ◽  
Scale Variation

Object tracking with robust scale estimation is a challenging task in computer vision. This paper presents a novel tracking algorithm that learns the translation and scale filters with a complementary scheme. The translation filter is constructed using the ridge regression and multidimensional features. A robust scale filter is constructed by the bidirectional scale estimation, including the forward scale and backward scale. Firstly, we learn the scale filter using the forward tracking information. Then the forward scale and backward scale can be estimated using the respective scale filter. Secondly, a conservative strategy is adopted to compromise the forward and backward scales. Finally, the scale filter is updated based on the final scale estimation. It is effective to update scale filter since the stable scale estimation can improve the performance of scale filter. To reveal the effectiveness of our tracker, experiments are performed on 32 sequences with significant scale variation and on the benchmark dataset with 50 challenging videos. Our results show that the proposed tracker outperforms several state-of-the-art trackers in terms of robustness and accuracy.

scholarly journals An Efficient and Robust Multi-Object Recognition and Tracking Algorithm using Mask Region based Convolution Neural Network (R-CNN)

2019 ◽  
Vol 8 (9) ◽  
pp. 607-613
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Object Recognition ◽  
Object Tracking ◽  
Ground Truth ◽  
Benchmark Dataset ◽  
Tracking Algorithm ◽  
Multiple Objects ◽  
High Quality ◽  
Extensive Simulation

Presently, Multi-Object tracking (MOT) is mainly applied for predicting the positions of many predefined objects across many successive frames with the provided ground truth position of the target in the first frame. The area of MOT gains more interest in the area of computer vision because of its applicability in various fields. Many works have been presented in recent years that intended to design a MOT algorithm with maximum accuracy and robustness. In this paper, we introduce an efficient as well as robust MOT algorithm using Mask R-CNN. The usage of Mask R-CNN effectively identifies the objects present in the image while concurrently creating a high-quality segmentation mask for every instance. The presented MOT algorithm is validated using three benchmark dataset and the results are extensive simulation. The presented tracking algorithm shows its efficiency to track multiple objects precisely

scholarly journals Part-Based Visual Tracking via Online Weighted P-N Learning

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Heng Fan ◽  
Jinhai Xiang ◽  
Jun Xu ◽  
Honghong Liao
Keyword(s):  
Visual Tracking ◽  
State Of The Art ◽  
The State ◽  
Tracking Algorithm ◽  
Local Feature ◽  
Experimental Results ◽  
Learning Method ◽  
Target Model ◽  
Tracking Process

We propose a novel part-based tracking algorithm using online weighted P-N learning. An online weighted P-N learning method is implemented via considering the weight of samples during classification, which improves the performance of classifier. We apply weighted P-N learning to track a part-based target model instead of whole target. In doing so, object is segmented into fragments and parts of them are selected as local feature blocks (LFBs). Then, the weighted P-N learning is employed to train classifier for each local feature block (LFB). Each LFB is tracked through the corresponding classifier, respectively. According to the tracking results of LFBs, object can be then located. During tracking process, to solve the issues of occlusion or pose change, we use a substitute strategy to dynamically update the set of LFB, which makes our tracker robust. Experimental results demonstrate that the proposed method outperforms the state-of-the-art trackers.

Multi-Object Tracking with Single Camera

2015 ◽  
Vol 740 ◽  
pp. 668-671
Author(s):  
Yu Bing Dong ◽  
Ying Sun ◽  
Ming Jing Li
Keyword(s):  
Computer Vision ◽  
Object Tracking ◽  
Block Matching ◽  
Tracking Algorithm ◽  
Experimental Results ◽  
Single Camera ◽  
Trajectory Prediction ◽  
Tracking Method

Multi-object tracking has been a challenging topic in computer vision. A Simple and efficient moving multi-object tracking algorithm is proposed. A new tracking method combined with trajectory prediction and a sub-block matching is used to handle the objects occlusion. The experimental results show that the proposed algorithm has good performance.

scholarly journals Distractor-Aware Deep Regression for Visual Tracking

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 387 ◽  
Author(s):  
Ming Du ◽  
Yan Ding ◽  
Xiuyun Meng ◽  
Hua-Liang Wei ◽  
Yifan Zhao
Keyword(s):  
Object Tracking ◽  
Visual Tracking ◽  
Test Data ◽  
Loss Function ◽  
State Of The Art ◽  
Target Object ◽  
Visual Object ◽  
Visual Object Tracking ◽  
Training Samples ◽  
Better Than

In recent years, regression trackers have drawn increasing attention in the visual-object tracking community due to their favorable performance and easy implementation. The tracker algorithms directly learn mapping from dense samples around the target object to Gaussian-like soft labels. However, in many real applications, when applied to test data, the extreme imbalanced distribution of training samples usually hinders the robustness and accuracy of regression trackers. In this paper, we propose a novel effective distractor-aware loss function to balance this issue by highlighting the significant domain and by severely penalizing the pure background. In addition, we introduce a full differentiable hierarchy-normalized concatenation connection to exploit abstractions across multiple convolutional layers. Extensive experiments were conducted on five challenging benchmark-tracking datasets, that is, OTB-13, OTB-15, TC-128, UAV-123, and VOT17. The experimental results are promising and show that the proposed tracker performs much better than nearly all the compared state-of-the-art approaches.

A Multi-Object Tracking Algorithm Based on Multi-Camera

2011 ◽  
Vol 135-136 ◽  
pp. 70-75
Author(s):  
Ming Xin Jiang ◽  
Hong Yu Wang ◽  
Chao Lin
Keyword(s):  
Computer Vision ◽  
Object Tracking ◽  
Real Time ◽  
Tracking Algorithm ◽  
Location Information ◽  
Multiple Cameras ◽  
Multiple Objects ◽  
Vanishing Points ◽  
Basic Aspect ◽  
Real Time Tracking

As a basic aspect of computer vision, reliable tracking of multiple objects is still an open and challenging issue for both theory studies and real applications. A novel multi-object tracking algorithm based on multiple cameras is proposed in this paper. We obtain the foreground likelihood maps in each view by modeling the background using the codebook algorithm. The view-to-view homographies are computed using several landmarks on the chosen plane. Then, we achieve the location information of multi-target at chest layer and realize the tracking task. The proposed algorithm does not require detecting the vanishing points of cameras, which reduces the complexity and improves the accuracy of the algorithm. The experimental results show that our method is robust to the occlusion and could satisfy the real-time tracking requirement.

scholarly journals When Pedestrian Detection Meets Nighttime Surveillance: A New Benchmark

2020 ◽  
Author(s):  
Xiao Wang ◽  
Jun Chen ◽  
Zheng Wang ◽  
Wu Liu ◽  
Shin'ichi Satoh ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Computer Vision ◽  
State Of The Art ◽  
Pedestrian Detection ◽  
Critical Time ◽  
Autonomous Driving ◽  
Benchmark Dataset ◽  
Lighting Conditions ◽  
Criminal Suspects

Pedestrian detection at nighttime is a crucial and frontier problem in surveillance, but has not been well explored by the computer vision and artificial intelligence communities. Most of existing methods detect pedestrians under favorable lighting conditions (e.g. daytime) and achieve promising performances. In contrast, they often fail under unstable lighting conditions (e.g. nighttime). Night is a critical time for criminal suspects to act in the field of security. The existing nighttime pedestrian detection dataset is captured by a car camera, specially designed for autonomous driving scenarios. The dataset for nighttime surveillance scenario is still vacant. There are vast differences between autonomous driving and surveillance, including viewpoint and illumination. In this paper, we build a novel pedestrian detection dataset from the nighttime surveillance aspect: NightSurveillance1. As a benchmark dataset for pedestrian detection at nighttime, we compare the performances of state-of-the-art pedestrian detectors and the results reveal that the methods cannot solve all the challenging problems of NightSurveillance. We believe that NightSurveillance can further advance the research of pedestrian detection, especially in the field of surveillance security at nighttime.

scholarly journals Visual Tracking Based on an Improved Online Multiple Instance Learning Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Li Jia Wang ◽  
Hua Zhang
Keyword(s):  
Visual Tracking ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Multiple Instance Learning ◽  
Tracking Algorithm ◽  
Inner Product ◽  
Selection Strategy ◽  
Weak Classifier ◽  
Update Strategy ◽  
Appearance Changes

An improved online multiple instance learning (IMIL) for a visual tracking algorithm is proposed. In the IMIL algorithm, the importance of each instance contributing to a bag probability is with respect to their probabilities. A selection strategy based on an inner product is presented to choose weak classifier from a classifier pool, which avoids computing instance probabilities and bag probabilityMtimes. Furthermore, a feedback strategy is presented to update weak classifiers. In the feedback update strategy, different weights are assigned to the tracking result and template according to the maximum classifier score. Finally, the presented algorithm is compared with other state-of-the-art algorithms. The experimental results demonstrate that the proposed tracking algorithm runs in real-time and is robust to occlusion and appearance changes.

An adaptive window object tracking algorithm based on variable resolution

2011 ◽  
Vol 19 (2) ◽  
Author(s):  
L. Li
Keyword(s):  
Object Tracking ◽  
Visual Tracking ◽  
Posterior Probability ◽  
Sampling Method ◽  
Tracking Algorithm ◽  
Variable Resolution ◽  
Tracking Method ◽  
Adaptive Window ◽  
Maximum Posterior Probability ◽  
Probability Index

AbstractThis paper presents an adaptive window object tracking method based on variable resolution. It copes with the change in size of the object during visual tracking. On the basis of the visual tracking algorithm, based on maximum posterior probability, we analyze the posterior probability index on the inside and outside panes of the object window, then build a mathematical model for adjusting object size with an adaptive window. Since the resolution changes according to the size of the object, this thesis uses a statistical sampling method of the feature by variable resolution. The resolution of the statistical feature is correspondingly changed in object tracking with an adaptive window. The resolution of a larger object is decreased, which realizes an object tracking method with adaptive window based on variable resolution.

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.

scholarly journals A Visual Tracking Method Based on an Adaptive Overlapping Correlation Filter for Robotic Real-Time Cognitive Imaging

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Yihua Lan ◽  
Pianpian Ma ◽  
Anfeng Xu ◽  
Jinjiang Liu
Keyword(s):  
Computer Vision ◽  
Success Rate ◽  
Visual Tracking ◽  
Estimation Method ◽  
Correlation Filter ◽  
Cognitive Computing ◽  
Tracking Accuracy ◽  
Tracking Method ◽  
Scale Estimation ◽  
Tracking Algorithms

Computer vision is a very important research direction in the cognitive computing field. Robots encounter various target-tracking problems with computer vision systems. Robust scale estimation is an important issue in tracking algorithms. Most of the available methods have difficulty addressing even reasonable changes of scale in complex videos. In this paper, we propose a visual tracking method based on robust scale estimation, which uses a discriminant correlation filter based on a time-dependent scale-space filter and an adaptive cross-correlation filter. The tracker uses separate essential filters for sample migration and scale estimation. Furthermore, the built-in scale estimation method can be introduced into other tracking algorithms. We validate the proposed method on the UAV123 dataset. The results of comparison experiments with the traditional correlation filter tracking method demonstrate that the proposed method improves the success rate and tracking accuracy while controlling the computational complexity; its success rate measured by the area under the curve is 0.638, while at a location error precision of 20%, it is 0.649.

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