Enhanced MIL Visual Tracking via Distribution Fields Descriptor

2014 ◽  
Vol 571-572 ◽  
pp. 725-728
Author(s):  
Kang Sun ◽  
Nian Nian Sun
Keyword(s):  
Visual Tracking ◽  
State Of The Art ◽  
Multiple Instance Learning ◽  
Experimental Results ◽  
Video Sequences ◽  
Object Model ◽  
Object Appearance ◽  
Distribution Field

This paper presents an enhanced multiple instance learning (MIL) tracker with a suitable representation of object appearance called distribution field descriptor (DF). To address transformations of object template (rotation, scaling), we firstly replace the smoothed histograms used in DF with smoothed bins according the theory of averaged shifted histograms. Secondly, due to the DF specificity and landscape smoothness, we adopt DF descriptor instead of traditional Haar-like one to represent the object appearance. By build object model using selected discriminative layers, our tracker is more robust while needing fewer features than the original tracker. The experimental results show higher performances of our tracker than those of five state-of-the-art ones on several challenging video sequences.

Robust Inland Waterway Ship Tracking via Hybrid TLD and Kalman Filter

2014 ◽  
Vol 1037 ◽  
pp. 373-377 ◽  
Author(s):  
Teng Fei ◽  
Liu Qing ◽  
Lin Zhu ◽  
Jing Li
Keyword(s):  
Kalman Filter ◽  
Visual Tracking ◽  
State Of The Art ◽  
Experimental Results ◽  
Closed Circuit ◽  
Video Sequences ◽  
Inland Waterway ◽  
Cluttered Background ◽  
Full Occlusion

In this paper, we mainly address the problem of tracking a single ship in inland waterway CCTV (Closed-Circuit Television) video sequences. Although state-of-the-art performance has been demonstrated in TLD (Tracking-Learning-Detection) visual tracking, it is still challenging to perform long-term robust ship tracking due to factors such as cluttered background, scale change, partial or full occlusion and so forth. In this work, we focus on tracking a single ship when it suffers occlusion. To accomplish this goal, an effective Kalman filter is adopted to construct a novel online model to adapt to the rapid ship appearance change caused by occlusion. Experimental results on numerous inland waterway CCTV video sequences demonstrate that the proposed algorithm outperforms the original one.

ROBUST EYE TRACKING IN VIDEO SEQUENCE

2012 ◽  
Vol 21 (01) ◽  
pp. 1250012 ◽  
Author(s):  
HUCHUAN LU ◽  
SHIPENG LU ◽  
GANG YANG
Keyword(s):  
Eye Tracking ◽  
Visual Tracking ◽  
Video Sequence ◽  
Experimental Results ◽  
Video Sequences ◽  
Affine Manifold ◽  
Second Stage ◽  
Novel Method ◽  
Iris Center ◽  
Image Streams

In this paper, we present a novel method for eye tracking, in detail describing the eye contour and the visible iris center. Combining the IVT (Incremental Visual Tracking) tracker, the proposed online affine manifold model, in which the sequentially learning shape and texture are modeled in the first stage and noniterative recovering estimation in the second stage, tracks the eye contour in video sequences. After that, an adaptive black round mask is generated to match the visible iris center. Experimental results of eye tracking indicate that our tracker works well in the PC or domestic camera captured image streams with considerable head and eyeball rotation.

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.

Dynamic Object Tracking Based on KAZE Features and Particle Filter

2014 ◽  
Vol 556-562 ◽  
pp. 2702-2706
Author(s):  
Ying Xia ◽  
Xin Hao Xu
Keyword(s):  
Particle Filter ◽  
Object Tracking ◽  
Scale Invariance ◽  
Experimental Results ◽  
Object Model ◽  
Dynamic Object ◽  
Color Features ◽  
Dynamic Tracking ◽  
Object Appearance ◽  
Accuracy And Stability

Accuracy and stability is crucial for dynamic object tracking. Considering the scale invariance, rotational invariance and strong anti-jamming capability of KAZE features, a method of dynamic object tracking based on KAZE features and particle filter is proposed. This method obtains the global color features of the dynamic object appearance and extracts its local KAZE features to construct the object model first, and then performs dynamic tracking by particle filter. Experimental results demonstrate the accuracy and stability of the proposed method.

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.

scholarly journals Visual Tracking of Small Unmanned Aerial Vehicles Based on Object Proposal Voting

2021 ◽  
Vol 11 (3) ◽  
pp. 953
Author(s):  
Jin Hong ◽  
Junseok Kwon
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Visual Tracking ◽  
State Of The Art ◽  
Experimental Results ◽  
Tracking Method ◽  
Aerial Vehicles ◽  
Object Proposal ◽  
Background Clutter ◽  
Art Performance ◽  
Over Time

In this paper, we propose a novel visual tracking method for unmanned aerial vehicles (UAVs) in aerial scenery. To track the UAVs robustly, we present a new object proposal method that can accurately determine the object regions that are likely to exist. The proposed object proposal method is robust to small objects and severe background clutter. For this, we vote on candidate areas of the object and increase or decrease the weight of the area accordingly. Thus, the method can accurately propose the object areas that can be used to track small-sized UAVs with the assumption that their motion is smooth over time. Experimental results verify that UAVs are accurately tracked even when they are very small and the background is complex. The proposed method qualitatively and quantitatively delivers state-of-the-art performance in comparison with conventional object proposal-based methods.

scholarly journals Visual Tracking Using Max-Average Pooling and Weight-Selection Strategy

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Suguo Zhu ◽  
Junping Du
Keyword(s):  
Particle Filter ◽  
Sparse Representation ◽  
Visual Tracking ◽  
Spatial Information ◽  
State Of The Art ◽  
Experimental Results ◽  
Selection Strategy ◽  
Illumination Change ◽  
Spatial Pooling ◽  
Hybrid Framework

Many modern visual tracking algorithms incorporate spatial pooling, max pooling, or average pooling, which is to achieve invariance to feature transformations and better robustness to occlusion, illumination change, and position variation. In this paper, max-average pooling method and Weight-selection strategy are proposed with a hybrid framework, which is combined with sparse representation and particle filter, to exploit the spatial information of an object and make good compromises to ensure the correctness of the results in this framework. Challenges can be well considered by the proposed algorithm. Experimental results demonstrate the effectiveness and robustness of the proposed algorithm compared with the state-of-the-art methods on challenging sequences.

scholarly journals Reliable Memory Model for Visual Tracking

Electronics ◽  
2021 ◽  
Vol 10 (20) ◽  
pp. 2488
Author(s):  
Daohui Ge ◽  
Ruyi Liu ◽  
Yunan Li ◽  
Qiguang Miao
Keyword(s):  
Visual Tracking ◽  
State Of The Art ◽  
Experimental Results ◽  
Memory Model ◽  
Background Information ◽  
Evaluation Strategy ◽  
Active Memory ◽  
Training Samples ◽  
Art Performance ◽  
Similarity Distance

Effectively learning the appearance change of a target is the key point of an online tracker. When occlusion and misalignment occur, the tracking results usually contain a great amount of background information, which heavily affects the ability of a tracker to distinguish between targets and backgrounds, eventually leading to tracking failure. To solve this problem, we propose a simple and robust reliable memory model. In particular, an adaptive evaluation strategy (AES) is proposed to assess the reliability of tracking results. AES combines the confidence of the tracker predictions and the similarity distance, which is between the current predicted result and the existing tracking results. Based on the reliable results of AES selection, we designed an active–frozen memory model to store reliable results. Training samples stored in active memory are used to update the tracker, while frozen memory temporarily stores inactive samples. The active–frozen memory model maintains the diversity of samples while satisfying the limitation of storage. We performed comprehensive experiments on five benchmarks: OTB-2013, OTB-2015, UAV123, Temple-color-128, and VOT2016. The experimental results show that our tracker achieves state-of-the-art performance.

Detecting “DeepFakes” in H.264 Video Data Using Compression Ghost Artifacts

2020 ◽  
Vol 2020 (4) ◽  
pp. 116-1-116-7
Author(s):  
Raphael Antonius Frick ◽  
Sascha Zmudzinski ◽  
Martin Steinebach
Keyword(s):  
Image Forensics ◽  
Video Data ◽  
Experimental Results ◽  
Video Sequences ◽  
The Internet ◽  
Video Content ◽  
High Quality ◽  
The Public

In recent years, the number of forged videos circulating on the Internet has immensely increased. Software and services to create such forgeries have become more and more accessible to the public. In this regard, the risk of malicious use of forged videos has risen. This work proposes an approach based on the Ghost effect knwon from image forensics for detecting forgeries in videos that can replace faces in video sequences or change the mimic of a face. The experimental results show that the proposed approach is able to identify forgery in high-quality encoded video content.

ОСОБЕННОСТИ АРХИТЕКТУРЫ И ОСНОВНЫЕ ХАРАКТЕРИСТИКИ АППАРАТНОГО ВИДЕОКОДЕКА СТАНДАРТА H.264 В СОСТАВЕ МИКРОСХЕМЫ 1892ВМ14Я

2020 ◽  
Vol 96 (3s) ◽  
pp. 89-96
Author(s):  
А.А. Беляев ◽  
Я.Я. Петричкович ◽  
Т.В. Солохина ◽  
И.А. Беляев
Keyword(s):  
Data Flow ◽  
System On Chip ◽  
Video Codec ◽  
Experimental Results ◽  
Video Sequences ◽  
Ip Core ◽  
Performance Measurements ◽  
Synchronization Mechanism ◽  
Architectural Features ◽  
On Chip

Рассмотрены особенности архитектуры и основные характеристики аппаратного видеокодека по стандарту H.264, входящего в состав микросхемы 1892ВМ14Я (MCom-02). Описан механизм синхронизации потоков данных на основе набора флагов событий. Приведены экспериментальные результаты измерения характеристик производительности разработанного видеокодека на реальных видеосюжетах при различных форматах передаваемого изображения. The paper considers main architectural features and characteristics of H.264 hardware video codec IP-core as a part of MCom- 02 system-on-chip (SoC). Bedides, it presents data flow synchronization mechanism based on event flags set, as well as experimental results of performance measurements for the designed video codec IP-core obtained for different video sequences and different image formats.

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