Multi-Object Tracking Research Topics

Multi-Object Tracking Research Topics is now widely used for the tracking of objects. It also tracks and identifies the multiple objects over a video sequence. Below are the details relevant to our proposed research.

1. Define Multi-Object Tracking

In the starting stage we first go through the definition for our proposed research; it is the process for identifying and incessantly succeeding multiple objects in time with videos or image sequences.

2. What is Multi-Object Tracking?

Thereafter the definition we see the deep explanation for the proposed research. It is a work that contains identifying and monitoring multiple objects over a sequence of video and is based on computer vision technology. The aim is to find and track down objects of concern in every structure and then connect them between the structures in time to preserve the way of their actions.

3. Where Multi-Object Tracking used?

Afterwards the deep explanation, we discuss where to utilize the Multi-Object Tracking system. It is used in the places like Robotics, Entertainment and Animation, Surveillance and Security, Agriculture, Autonomous Vehicles, Retail and Customer Analytics, Traffic analysis, Crowd management, Sports Analysis and Medical Imaging are the places where Multi-object tracking technique is used.

4. Why Multi-Object Tracking technology proposed? , Previous technology issues

In this research, the Multi-Object Tracking technology is proposed to improve conditional perception, enhance safety and resource allocation by exactly identifying and controlling the movements and communications of multiple objects in different domains from surveillance to independent framework. Several previous technology issues that contain are ineffective segmentation, Poor QoS, Lack of image quality assessment and inaccurate path selection.

5. Algorithms / protocols

For our proposed research Multi-Object Tracking technology we utilize the methods like Normalized Gamma Transformation based CLAHE (NGT-CLAHE), Energy Valley Optimizer (EVO), Improved Adaptive Extended Kalman Filter (IAEKF), YOLO V7, Deep Q Network (DQN), LIGHT G Net, Dense Net (D Net) and Improved Adaptive Weighted Mean Filter (IAWMF) are the methods that we utilized for this proposed research.

6. Comparative study / Analysis

We propose a novel technology that has many features as compared to the previous methods. Several methods that we utilized for this research are as follows:

• For noise reduction we utilize Improved Adaptive Extended Kalman Filter (IAEKF) is utilized and for contrast improvement we employ Normalized Gamma Transformation based CLAHE (NGT-CLAHE) and for Adaptive thresholding we use Improved Adaptive Weighted Mean Filter (IAWMF).
• Dense Net based multi-image fusion provides high efficiency and an increased memory of speed processing time. To choose grid map based routes and tracks Energy Valley Optimizer (EVO) is used. This strategy solves difficult tasks in an easy way, which causes simple flexibility, scalability and resilience. Moreover the YOLO V7 method is utilized for identification and classification.

7. Simulation results / Parameters

The proposed Multi-Object Tracking technology is utilized to overcome several existing technology issues. In this we compare some parameters or performance metrics to get the better results. Mean Squared Error, Accuracy, Success ratio, Velocity, Accuracy rate and Success rate are the metrics that are used to obtain the best outcome.

8. Dataset LINKS / Important URL

Multi-Object Tracking technology is proposed in this research and here we offer links that are related to our proposed research and we can overview the links to clarify the doubts.

• https://pubsonline.informs.org/doi/abs/10.1287/msom.2020.0939
• https://www.mdpi.com/1424-8220/22/11/4212

9. Multi-Object Tracking Applications

Agriculture, Sports Analysis, Entertainment and Animation, Medical Imaging, Retail and Customer Analysis and medical Imaging are the applications that are utilized for this proposed research into multi-object tracking technology.

10. Topology for Multi-Object Tracking

Now we see the topology that is used for this research are Validation and Testing, Redundancy and Safety Measure, Sensor Fusion, Human Interaction, Pre-Processing, Object Association, Motion forecasting, Semantic Segmentation, Object identification and Map Integration.

11. Environment in Multi-Object Tracking

Let’s discuss the environment that is employed for this research is Localization and Mapping, Data Collection, Object Models, Testing and Validation, Simulation Platform, Sensor Degradation, Data Annotation, Weather Simulation, Sensor Models, Object Behavior, and Challenging Scenarios.

12. Simulation Tool

The proposed research is executed by incorporating the succeeding needs. Here we offer the simulation tool or the software requirement that is required for this research. The tool that is employed to implement our proposed research is MATLAB R2020a. The operating system used for this research is Windows 10 Pro.

13. Results

Here we propose the Multi-Object Tracking technology; it involves identifying and monitoring multiple objects over video sequence. In this we contrast various performance metrics with existing technologies and verifying our proposed research will give the best outcome when compared to others. This can be implemented by using the tool MATLAB R2020a.

Multi-Object Tracking Research Ideas:

We provide some important research topics below, that are related to our proposed research on a Multi-Object Tracking technique. These topics are helpful to us when we have queries about this technique.

1. TunnelTrack: A Dataset for Multi-Object Tracking in Tunnel Roads
2. UGV-UAV Cooperative 3D Multi-Object Tracking Based on Multi-Source Data Fusion
3. Research on Multi-object Tracking Algorithm Based on Person Recognition and Detection Fusion
4. Automated Grapevine Inflorescence Counting in a Vineyard Using Deep Learning and Multi-object Tracking
5. Multi-vehicle Speed Measurement Based on Monitoring Video Using Improved Multi-object Tracking Algorithm
6. An Optimized Multi-Object Tracking with TensorRT
7. A Deep Multi-Object Tracking Technique in Swimming Video Scenes
8. Improved Kalman Filter and Matching Strategy for Multi-Object Tracking System
9. Pedestrian Multi-Object Tracking with Bottleneck Transformer and Enhanced Feature Fusion
10. 3D Multi-Object Tracking based on Two-Stage Data Association for Collaborative Perception Scenarios
11. Multi-Object Tracking by Iteratively Associating Detections with Uniform Appearance for Trawl-Based Fishing Bycatch Monitoring
12. Online Action Detection in Surveillance Scenarios: A Comprehensive Review and Comparative Study of State-of-the-Art Multi-Object Tracking Methods
13. CCDMOT: An Optimized Multi-Object Tracking Method for Unmanned Vehicles Pedestrian Tracking
14. CAMO-MOT: Combined Appearance-Motion Optimization for 3D Multi-Object Tracking With Camera-LiDAR Fusion
15. 3D Multi-Object Tracking With Adaptive Cubature Kalman Filter for Autonomous Driving
16. Multi-Object Tracking Based on Prediction of Invisible Object Trajectories
17. Looking Beyond Two Frames: End-to-End Multi-Object Tracking Using Spatial and Temporal Transformers
18. DC-MOT: Motion Deblurring and Compensation for Multi-Object Tracking in UAV Videos
19. Multi-Object Tracking Based on a Novel Feature Image With Multi-Modal Information
20. Rethinking Multi-Object Tracking Based on Re-Identification and Appearance Model Management
21. A Multi-Object Tracking Algorithm With Center-Based Feature Extraction and Occlusion Handling
22. FFTransMOT: Feature-Fused Transformer for Enhanced Multi-Object Tracking
23. Robust Multi-Object Tracking With Local Appearance and Stable Motion Models
24. Semantically Enhanced Multi-Object Detection and Tracking for Autonomous Vehicles
25. Multi-Object Tracking as Attention Mechanism
26. Feature Compression for Multimodal Multi-Object Tracking
27. StrongFusionMOT: A Multi-Object Tracking Method Based on LiDAR-Camera Fusion
28. 3D Multi-Object Tracking Based on Dual-Tracker and D-S Evidence Theory
29. Multi Object Tracking System form Video Streaming using Yolo
30. Multi-object Detection, Tracking and Prediction in Rugged Dynamic Environments
31. Person Re-Identification for Multi-Camera, Multi-Object Tracking on Robotic Platforms
32. Radar Multi Object Tracking using DNN Features
33. Multi-Object Tracking: Decoupling Features to Solve the Contradictory Dilemma of Feature Requirements
34. Jointing Recurrent Across-Channel and Spatial Attention for Multi-Object Tracking With Block-Erasing Data Augmentation
35. SFFSORT Multi-Object Tracking by Shallow Feature Fusion for Vehicle Counting
36. Standing Between Past and Future: Spatio-Temporal Modeling for Multi-Camera 3D Multi-Object Tracking
37. CFTracker: Multi-Object Tracking With Cross-Frame Connections in Satellite Videos
38. Multi-class multi-object detection and tracking system in 3 dimensions using YOLOv5
39. Multi-Object Tracking by Self-supervised Learning Appearance Model
40. InterTrack: Interaction Transformer for 3D Multi-Object Tracking
41. Multi-object Tracking based on improved YOLO
42. GPU Acceleration of Multi-Object Tracking with Motion Vector Interpolation and Affine Transformation
43. A Note on Improvement of Multi Object Tracking by Frame Interpolation for Intersection Traffic
44. Split and Connect: A Universal Tracklet Booster for Multi-Object Tracking
45. A Simple but Effective Method for Balancing Detection and Re-Identification in Multi-Object Tracking
46. Poly-MOT: A Polyhedral Framework For 3D Multi-Object Tracking
47. Learning to Reconnect Interrupted Trajectories for Weakly Supervised Multi-Object Tracking
48. JDT-NAS: Designing Efficient Multi-Object Tracking Architectures for Non-GPU Computers
49. MotionTrack: Learning Robust Short-Term and Long-Term Motions for Multi-Object Tracking
50. AttTrack: Online Deep Attention Transfer for Multi-object Tracking