scholarly journals A Unified Object Motion and Affinity Model for Online Multi-Object Tracking

2020 ◽  
Author(s):  
Junbo Yin ◽  
Wenguan Wang ◽  
Qinghao Meng ◽  
Ruigang Yang ◽  
Jianbing Shen
Keyword(s):  
Object Tracking ◽  
Object Motion ◽  
Affinity Model

scholarly journals Deep Learning of Appearance Affinity for Multi-Object Tracking and Re-Identification: A Comparative View

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1757
Author(s):  
María J. Gómez-Silva ◽  
Arturo de la Escalera ◽  
José M. Armingol
Keyword(s):  
Deep Learning ◽  
Object Tracking ◽  
Loss Function ◽  
Neural Model ◽  
Training Data ◽  
Learning Approaches ◽  
The Core ◽  
Triplet Loss ◽  
Affinity Model

Recognizing the identity of a query individual in a surveillance sequence is the core of Multi-Object Tracking (MOT) and Re-Identification (Re-Id) algorithms. Both tasks can be addressed by measuring the appearance affinity between people observations with a deep neural model. Nevertheless, the differences in their specifications and, consequently, in the characteristics and constraints of the available training data for each one of these tasks, arise from the necessity of employing different learning approaches to attain each one of them. This article offers a comparative view of the Double-Margin-Contrastive and the Triplet loss function, and analyzes the benefits and drawbacks of applying each one of them to learn an Appearance Affinity model for Tracking and Re-Identification. A batch of experiments have been conducted, and their results support the hypothesis concluded from the presented study: Triplet loss function is more effective than the Contrastive one when an Re-Id model is learnt, and, conversely, in the MOT domain, the Contrastive loss can better discriminate between pairs of images rendering the same person or not.

Visual Tracking Using Combining Motion Constraint Model and Online Multiple Instance Boost Random Ferns

2012 ◽  
Vol 263-266 ◽  
pp. 2385-2392
Author(s):  
He Rong Zheng ◽  
Ye Jue Huang
Keyword(s):  
Object Tracking ◽  
Tracking Error ◽  
Object Motion ◽  
Motion Constraints ◽  
Video Object Tracking ◽  
Iir Filter ◽  
Error Accumulation ◽  
Constraint Model ◽  
Constraints Model ◽  
Motion Constraint

Video object tracking is essential algorithm for computer vision applications. An object tracking algorithm using combining motion constraints model and online multiple instance boost random ferns is proposed, which use IIR filter to obtain online learning for random ferns, and the random ferns are selected by online multiple instance boosting to construct classifier of online multiple instance boost random ferns. To reduce effects of tracking error accumulation, object motion constraint model is constructed to constrain the results classified by online multiple instance boost random ferns to locate object correctly, and construct positive and negative set to online update the classifier. The experiment shows that the proposed method achieves competitive detection results, which are comparable with state-of-the-art methods.

scholarly journals Object Tracking Using Adaptive Diffusion Flow Active Model

2021 ◽  
Vol 17 (10) ◽  
pp. 17
Author(s):  
Israa A. Alwan ◽  
Faaza A. Almarsoomi
Keyword(s):  
Object Tracking ◽  
Moving Objects ◽  
Target Object ◽  
Object Motion ◽  
Tracking Algorithm ◽  
Video Frame ◽  
Diffusion Flow ◽  
Motion Direction ◽  
Image Domain ◽  
Active Model

Object tracking is one of the most important topics in the fields of image processing and computer vision. Object tracking is the process of finding interesting moving objects and following them from frame to frame. In this research, Active models–based object tracking algorithm is introduced. Active models are curves placed in an image domain and can evolve to segment the object of interest. Adaptive Diffusion Flow Active Model (ADFAM) is one the most famous types of Active Models. It overcomes the drawbacks of all previous versions of the Active Models specially the leakage problem, noise sensitivity, and long narrow hols or concavities. The ADFAM is well known for its very good capabilities in the segmentation process. In this research, it is adopted for segmentation and tracking purposes. The proposed object tracking algorithm is initiated by detecting the target moving object manually. Then, the ADFAM convergence of the current video frame is reused as an initial estimation for the next video frame and so on. The proposed algorithm is applied to several video sequences, different in terms of the nature of the object, the nature of the background, the speed of the object, object motion direction, and the inter-frame displacement. Experimental results show that the proposed algorithm performed very well and successfully tracked the target object in all different cases.

scholarly journals Individual Differences in Visual Attention: A Short, Reliable, Open Source, and Multilingual Test of Multiple Object Tracking in PsychoPy

2020 ◽  
Author(s):  
Hauke S. Meyerhoff ◽  
Frank Papenmeier
Keyword(s):  
Individual Differences ◽  
Visual Attention ◽  
Object Tracking ◽  
Open Source ◽  
Multiple Object Tracking ◽  
Object Motion ◽  
Laboratory Research ◽  
Multiple Object ◽  
Psychometric Measures ◽  
Interesting Approach

Individual differences in attentional abilities provide an interesting approach in studying visual attention as well as the relation of attention to other psychometric measures. However, recent research has demonstrated that many tasks from experimental research are not suitable for individual differences research as they fail to capture these differences reliably. Here, we provide a test for individual differences in visual attention which relies on the multiple object tracking task (MOT). This test captures individual differences reliably in 6-15 minutes. Within the task the participants have to maintain a set of targets (among identical distractors) across an interval of object motion. It captures the efficiency of attentional deployment. Importantly, this test was explicitly designed and tested for reliability under conditions that match those of most laboratory research (restricted sample of students, approximately n = 50). The test is free to use and runs fully under open source software. In order to facilitate the application of the test, we have translated it into 16 common languages (Chinese, Danish, Dutch, English, Finnish, French, German, Italian, Japanese, Norwegian, Polish, Portuguese, Russian, Spanish, Swedish, Turkish). The test can be downloaded at https://osf.io/qy6nb/. We hope that this MOT test supports researchers whose field of study requires capturing individual differences in visual attention reliably.

scholarly journals Machine Vision Enabled Bot for Object Tracking

2020 ◽  
Vol 1 (1) ◽  
pp. 15-26
Author(s):  
Rupali Patil ◽  
Adhish Velingkar ◽  
Mohammad Nomaan Parmar ◽  
Shubham Khandhar ◽  
Bhavin Prajapati
Keyword(s):  
Machine Learning ◽  
Object Tracking ◽  
Cloud Storage ◽  
Object Motion ◽  
Human Machine Interaction ◽  
Camera Control ◽  
Live Feed ◽  
Detection And Tracking ◽  
Left And Right ◽  
Machine Interaction

Object detection and tracking are essential and testing undertaking in numerous PC vision appliances. To distinguish the object first find a way to accumulate information. In this design, the robot can distinguish the item and track it just as it can turn left and right position and afterward push ahead and in reverse contingent on the object motion. It keeps up the consistent separation between the item and the robot. We have designed a webpage that is used to display a live feed from the camera and the camera can be controlled by the user efficiently. Implementation of machine learning is done for detection purposes along with open cv and creating cloud storage. The pan-tilt mechanism is used for camera control which is attached to our 3-wheel chassis robot through servo motors. This idea can be used for surveillance purposes, monitoring local stuff, and human-machine interaction.

OBJECT MOTION DETECTION AND TRACKING BY AN ARTIFICIAL INTELLIGENCE APPROACH

2008 ◽  
Vol 22 (05) ◽  
pp. 915-928 ◽  
Author(s):  
LUCIA MADDALENA ◽  
ALFREDO PETROSINO ◽  
ALESSIO FERONE
Keyword(s):  
Artificial Intelligence ◽  
Object Detection ◽  
Object Tracking ◽  
Moving Object Detection ◽  
Multiple Hypothesis Testing ◽  
Object Motion ◽  
Moving Object ◽  
Surveillance Systems ◽  
Robust Detection ◽  
Detection And Tracking

The aim of this paper is to propose an artificial intelligence based approach to moving object detection and tracking. Specifically, we adopt an approach to moving object detection based on self organization through artificial neural networks. Such approach allows to handle scenes containing moving backgrounds and gradual illumination variations, and achieves robust detection for different types of videos taken with stationary cameras. Moreover, for object tracking we propose a suitable conjunction between Kalman filtering, properly instanced for the problem at hand, and a matching model belonging to the class of Multiple Hypothesis Testing. To assess the validity of our approach, we experimented both proposed moving object detection and object tracking over different color video sequences that represent typical situations critical for video surveillance systems.

scholarly journals Robust Visual Multi Target Trackers: A Review

2018 ◽  
Vol 12 (1) ◽  
pp. 7
Author(s):  
Pavan Kumar E ◽  
Manojkumar Rajgopal
Keyword(s):  
Object Tracking ◽  
Video Surveillance ◽  
Review Paper ◽  
Object Motion ◽  
The State ◽  
Visual Object ◽  
Visual Object Tracking ◽  
Military Applications ◽  
State Of Art ◽  
Critical Challenge

<p>In this review paper, we address on the state-of-art process with visual object tracking in video surveillance, medical and military applications. In the present scenario number of algorithms and methods are used to track the object in the different scene, a robust visual object tracking remains a critical challenge. The challenges arise due to object motion from frame to frame with a change in appearance, structures, illumination, and occlusion. In this paper, at last, we outline the different algorithms, dataset, strength, and weakness of the different object tracker.</p>

scholarly journals Object Modelling and Tracking in Videos via Multidimensional Features

2011 ◽  
Vol 2011 ◽  
pp. 1-15 ◽  
Author(s):  
Zhuhan Jiang
Keyword(s):  
Bayesian Inference ◽  
Object Tracking ◽  
Video Sequence ◽  
Object Motion ◽  
Video Sequences ◽  
Current Frame ◽  
Object Modelling ◽  
Previous Frame ◽  
Object Features ◽  
Probabilistic Location

We propose to model a tracked object in a video sequence by locating a list of object features that are ranked according to their ability to differentiate against the image background. The Bayesian inference is utilised to derive the probabilistic location of the object in the current frame, with the prior being approximated from the previous frame and the posterior achieved via the current pixel distribution of the object. Consideration has also been made to a number of relevant aspects of object tracking including multidimensional features and the mixture of colours, textures, and object motion. The experiment of the proposed method on the video sequences has been conducted and has shown its effectiveness in capturing the target in a moving background and with nonrigid object motion.

scholarly journals End-to-End Learning of Object Motion Estimation from Retinal Events for Event-Based Object Tracking

2020 ◽  
Vol 34 (07) ◽  
pp. 10534-10541
Author(s):  
Haosheng Chen ◽  
David Suter ◽  
Qiangqiang Wu ◽  
Hanzi Wang
Keyword(s):  
Motion Estimation ◽  
Object Tracking ◽  
Linear Time ◽  
Main Idea ◽  
Object Motion ◽  
Art Object ◽  
End To End ◽  
Spatio Temporal ◽  
Event Based ◽  
Object Level

Event cameras, which are asynchronous bio-inspired vision sensors, have shown great potential in computer vision and artificial intelligence. However, the application of event cameras to object-level motion estimation or tracking is still in its infancy. The main idea behind this work is to propose a novel deep neural network to learn and regress a parametric object-level motion/transform model for event-based object tracking. To achieve this goal, we propose a synchronous Time-Surface with Linear Time Decay (TSLTD) representation, which effectively encodes the spatio-temporal information of asynchronous retinal events into TSLTD frames with clear motion patterns. We feed the sequence of TSLTD frames to a novel Retinal Motion Regression Network (RMRNet) to perform an end-to-end 5-DoF object motion regression. Our method is compared with state-of-the-art object tracking methods, that are based on conventional cameras or event cameras. The experimental results show the superiority of our method in handling various challenging environments such as fast motion and low illumination conditions.

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