scholarly journals The Application of Image Processing to Solve Occlusion Issue in Object Tracking

2018 ◽  
Vol 152 ◽  
pp. 03001
Author(s):  
Yun Zhe Cheong ◽  
Wei Jen Chew
Keyword(s):  
Object Tracking ◽  
Moving Objects ◽  
Object Segmentation ◽  
Target Object ◽  
Occlusion Handling ◽  
Multiple Objects ◽  
The Road ◽  
Occluded Objects ◽  
On The Road ◽  
Tracking Object

Object tracking is a computer vision field that involves identifying and tracking either a single or multiple objects in an environment. This is extremely useful to help observe the movements of the target object like people in the street or cars on the road. However, a common issue with tracking an object in an environment with many moving objects is occlusion. Occlusion can cause the system to lose track of the object being tracked or after overlapping, the wrong object will be tracked instead. In this paper, a system that is able to correctly track occluded objects is proposed. This system includes algorithms such as foreground object segmentation, colour tracking, object specification and occlusion handling. An input video is input to the system and every single frame of the video is analysed. The foreground objects are segmented with object segmentation algorithm and tracked with the colour tracking algorithm. An ID is assigned to each tracked object. Results obtained shows that the proposed system is able to continuously track an object and maintain the correct identity even after is has been occluded by another object.

Tracking Multiple Objects Is Limited Only by Object Spacing, Not by Speed, Time, or Capacity

2010 ◽  
Vol 21 (7) ◽  
pp. 920-925 ◽  
Author(s):  
S.L. Franconeri ◽  
S.V. Jonathan ◽  
J.M. Scimeca
Keyword(s):  
Object Tracking ◽  
Moving Objects ◽  
Multiple Object Tracking ◽  
Single Object ◽  
Multiple Objects ◽  
Performance Constraints ◽  
Root Cause ◽  
Multiple Object ◽  
Tracking Time ◽  
Environment Performance

In dealing with a dynamic world, people have the ability to maintain selective attention on a subset of moving objects in the environment. Performance in such multiple-object tracking is limited by three primary factors—the number of objects that one can track, the speed at which one can track them, and how close together they can be. We argue that this last limit, of object spacing, is the root cause of all performance constraints in multiple-object tracking. In two experiments, we found that as long as the distribution of object spacing is held constant, tracking performance is unaffected by large changes in object speed and tracking time. These results suggest that barring object-spacing constraints, people could reliably track an unlimited number of objects as fast as they could track a single object.

Multiple Objects Tracking Based on Mixture Features

2014 ◽  
Vol 945-949 ◽  
pp. 1869-1874
Author(s):  
Dong Mei Li ◽  
Tao Li
Keyword(s):  
Target Tracking ◽  
Moving Objects ◽  
Coincidence Degree ◽  
Multiple Target Tracking ◽  
Multiple Target ◽  
Multiple Objects ◽  
Current Frame ◽  
Complex Scenes ◽  
Objects Tracking ◽  
Tracking Object

For multiple objects tracking in complex scenes, this paper proposes a new tracking algorithm for multiple moving objects. The algorithm makes likelihood calculation by using new DG_CENTRIST feature and color feature, and then calculates the overlapping ratio of the tracking object and the object in the current frame using coincidence degree to measure the occlusion. The algorithm has good robustness and stability, and the experiment results show that this method can effectively improve the accuracy of the multiple target tracking.

scholarly journals Pedestrian-safer IoT-based Smart Crossing System with Object Tracking

2020 ◽  
Vol 9 (1) ◽  
pp. 1948-1953
Keyword(s):  
Object Tracking ◽  
Cost Effective ◽  
Point Of View ◽  
The Internet ◽  
Road Accidents ◽  
The Road ◽  
On The Road ◽  
Road Crossing ◽  
The Internet Of Things ◽  
Populous Country

In the field of technical research the Internet of Things (IoT) has become an interesting topic. The device is interconnected over the internet. We usually think of IoT in terms of independently owned cars and smart homes, but in extreme practical matters one of the best applications of IoT technology. In many disciplines, IoT is increasing rapidly from a technical point of view, in particular with the smart crossing system. In the meantime, it is a very populous country in Bangladesh. A lot of people cross the street every day. A lot of wide roads are to be crossed in Bangladesh. Even dead troubles. There is a lot of vehicles on the lane. There are many wide roads in Bangladesh that are a lot to cross. Troubles, even dead ones. Many vehicles are on the road. Bangladesh is also a developing country, and the laws of road crossing are not very strict, in which case it is very important to have a pedestrian-safer IoT-based smart crossing system with object tracking. Often people are facing an accident, in particular school children have trouble crossing the road, old people face the same problem. A cost-effective solution to this issue is the key contribution of this paper using a simple framework based on Arduino UNO R3. The device is fully autonomous and can calculate the planned parameters of a pedestrianized IoT-based, smart crossing platform with object tracking in an efficient way. Ultrasonic sensors and one IR sensor were used for measuring the parameters needed for the device. Moreover, in Bangladesh this program is more important and essential. This smart crossing system detects people as well as reduce road accidents.

Multiple Objects Tracking Based on Linear Fitting

2014 ◽  
Vol 602-605 ◽  
pp. 1438-1441
Author(s):  
Dong Mei Li ◽  
Tao Li ◽  
Tao Xiang ◽  
Wei Xu
Keyword(s):  
Moving Objects ◽  
Coincidence Degree ◽  
Multiple Target Tracking ◽  
Multiple Target ◽  
Multiple Objects ◽  
Current Frame ◽  
Linear Fitting ◽  
Complex Scenes ◽  
Objects Tracking ◽  
Tracking Object

For multiple objects tracking in complex scenes, a new tracking algorithm based on linear fitting for multiple moving objects is proposed. DG_CENTRIST feature and color feature are combined to describe the object, and the overlapping ratio of the tracking object is calculated. The object in the current frame is measured by using coincidence degree. If there is occlusion, we predict the path of each object by linear fitting and adjust the results of tracking in order to get correct results. The experiment results show that this method can effectively improve the accuracy of the multiple target tracking.

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.

Fast Automatic Video Object Segmentation for Content-Based Applications

2006 ◽  
pp. 140-160 ◽  
Author(s):  
Ee Ping Ong ◽  
Weisi Lin ◽  
Bee June Tye ◽  
Minoru Etoh
Keyword(s):  
Change Detection ◽  
Reference Frame ◽  
Moving Objects ◽  
Object Segmentation ◽  
Video Object Segmentation ◽  
Occluded Objects ◽  
Fully Automatic ◽  
Reference Frame Selection ◽  
Stationary Background

An algorithm has been devised for fast, fully automatic and reliable object segmentation from live video for scenarios with static camera. The contributions in this chapter include methods for: (a) adaptive determination of the threshold for change detection; (b) robust stationary background reference frame generation, which when used in change detection can reduce segmentation fault rate and solve the problems of dis-occluded objects appearing as part of segmented moving objects; (c) adaptive reference frame selection to improve segmentation results; and (d) spatial refinement of modified change detection mask by incorporating information from edges, gradients and motion to improve accuracy of segmentation contours. The algorithm is capable of segmenting multiple objects at a speed of 12 QCIF frames per second with a Pentium-4 2.8GHz personal computer in C coding without resorting to any code optimization. The result shows advantages over related work in terms of both fault rate and processing speed.

Multiple Object Tracking by Scale Space Representation of Objects, Method of Linear Assignment, and Kalman Filter

2016 ◽  
pp. 236-259
Author(s):  
Kumar S. Ray ◽  
Soma Ghosh ◽  
Kingshuk Chatterjee ◽  
Debayan Ganguly
Keyword(s):  
Kalman Filter ◽  
Object Tracking ◽  
Moving Objects ◽  
Tracking System ◽  
Scale Space ◽  
Space Representation ◽  
Occluded Objects ◽  
Video Scene ◽  
Cost Parameters ◽  
Linear Assignment

This chapter presents a multi-object tracking system using scale space representation of objects, the method of linear assignment and Kalman filter. In this chapter basically two very prominent problems of multi object tracking have been resolved; the two prominent problems are (i) irrespective of the size of the objects, tracking all the moving objects simultaneously and (ii) tracking of objects under partial and/or complete occlusion. The primary task of tracking multiple objects is performed by the method of linear assignment for which few cost parameters are computed depending upon the extracted features of moving objects in video scene. In the feature extraction phase scale space representation of objects have been used. Tracking of occluded objects is performed by Kalman filter.

Multi-Object Tracking and Occlusion Reasoning Based on Adaptive Weighing Particle Filter

2014 ◽  
Vol 10 (03) ◽  
pp. 225-238 ◽  
Author(s):  
Chenguang Liu ◽  
Heng-Da Cheng
Keyword(s):  
Particle Filter ◽  
Object Tracking ◽  
Challenging Problem ◽  
Multiple Objects ◽  
Occluded Objects ◽  
Occlusion Model ◽  
Occlusion Reasoning

Multi-object tracking has significant merit to our society. However, interactions among objects result in complex spatial occlusions, which gives rise to a challenging problem in tracking. We propose an adaptive weighing particle filter (AWPT) for tracking multiple objects and reasoning the occlusions among them. A weighing-occlusion modeling-weighing procedure is developed to adaptively weigh the particles. Moreover, we propose a stack occlusion model and define the operations on it to maintain the occlusion relationship. The experiments exhibit that the proposed method can effectively track fully occluded objects and reason about the occlusion relationships among them.

scholarly journals A Grid-Based Framework for Collective Perception in Autonomous Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (3) ◽  
pp. 744
Author(s):  
Jorge Godoy ◽  
Víctor Jiménez ◽  
Antonio Artuñedo ◽  
Jorge Villagra
Keyword(s):  
Object Tracking ◽  
Autonomous Vehicles ◽  
Line Of Sight ◽  
The Road ◽  
Occupancy Grid ◽  
One Step ◽  
On The Road ◽  
High Level ◽  
Share Information ◽  
Grid Based

Today, perception solutions for Automated Vehicles rely on sensors on board the vehicle, which are limited by the line of sight and occlusions caused by any other elements on the road. As an alternative, Vehicle-to-Everything (V2X) communications allow vehicles to cooperate and enhance their perception capabilities. Besides announcing its own presence and intentions, services such as Collective Perception (CPS) aim to share information about perceived objects as a high-level description. This work proposes a perception framework for fusing information from on-board sensors and data received via CPS messages (CPM). To that end, the environment is modeled using an occupancy grid where occupied, and free and uncertain space is considered. For each sensor, including V2X, independent grids are calculated from sensor measurements and uncertainties and then fused in terms of both occupancy and confidence. Moreover, the implementation of a Particle Filter allows the evolution of cell occupancy from one step to the next, allowing for object tracking. The proposed framework was validated on a set of experiments using real vehicles and infrastructure sensors for sensing static and dynamic objects. Results showed a good performance even under important uncertainties and delays, hence validating the viability of the proposed framework for Collective Perception.

A New Modeling Approach for the Video Pre-Analysis of Video Surveillance Systems

2021 ◽  
Vol 12 (3) ◽  
pp. 21-34
Author(s):  
Hocine Chebi
Keyword(s):  
Object Tracking ◽  
Video Surveillance ◽  
Surveillance System ◽  
Moving Objects ◽  
Video Surveillance System ◽  
Surveillance Systems ◽  
Analysis Method ◽  
Modeling Approach ◽  
Occluded Objects ◽  
Analysis Technique

The work presented in this paper aims to develop a new architecture for video surveillance systems. Among the problems encountered when tracking and classifying objects are groups of occluded objects. Simplifying the representation of objects leads to other reliable object tracking with smaller amounts of information used but protection of the necessary characteristics. Therefore, modeling moving objects into a simpler form can be considered a pre-analysis technique. Objects can be represented in different ways, and the choice of the representation of an object strongly depends on the field of application. An example of a video surveillance system respecting this architecture and using the pre-analysis method is proposed.

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