Improvement of Vision-based Drone Detection and Tracking by Removing Cluttered Background, Shadow and Water Reflection with Super Resolution

Motion analysis plays an important role in studing activities or behaviors of live objects in medicine, biotechnology, chemistry, physics, spectroscopy, nanotechnology, enzymology, and biological engineering. This paper briefly reviews the developments in this area mostly in the recent three years, especially for cellular analysis in fluorescence microscopy. The topic has received much attention with the increasing demands in biomedical applications. The tasks of motion analysis include detection and tracking of objects, as well as analysis of motion behavior, living activity, events, motion statistics, and so forth. In the last decades, hundreds of papers have been published in this research topic. They cover a wide area, such as investigation of cell, cancer, virus, sperm, microbe, karyogram, and so forth. These contributions are summarized in this review. Developed methods and practical examples are also introduced. The review is useful to people in the related field for easy referral of the state of the art.

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Super-Resolution Target Detection and Tracking.

10.21236/ada307013 ◽

1993 ◽

Author(s):

James A. Cadzow

Keyword(s):

Target Detection ◽

Super Resolution ◽

Detection And Tracking

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Detection and Tracking of Vehicle Target Based on Super-Resolution Reconstruction and Variable Template Matching

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.541-542.1429 ◽

2014 ◽

Vol 541-542 ◽

pp. 1429-1432

Author(s):

Jun Yong Ma ◽

Shao Dong Chen ◽

Sheng Wei Zhang

Keyword(s):

Template Matching ◽

Super Resolution ◽

Image Sequence ◽

Low Resolution ◽

Matching Method ◽

Matching Accuracy ◽

High Quality ◽

Resolution Image ◽

Detection And Tracking ◽

Image Super Resolution

Vehicle Target Detection and Tracking Method Based on Image Super-Resolution Reconstruction and Variable Template Matching is Put Forward. Firstly, a Nonlinear Iterative Algorithm is Applied to Reconstruct a Super-Resolution Image from Low Resolution Image Sequence; then, the Image is Standardized and the Movement Areas are Determined; Finally, the Variable Template Matching Method is Used to Detect and Track the Vehicle Targets in Movement Areas. from the Characteristics of Algorithm and the Experiment Results, we can see that the Proposed Algorithm Improves the Matching Accuracy of Target Tracking and Better Solves the Limitation of Missed Detection for Traditional Methods. the Reason of the Good Performance of the Proposed Algorithm Relies in High Quality Images Acquired by Super-Resolution Reconstruction from Low Resolution Image Sequence and the Application of Variable Template Matching Method.

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Multiple Detection and Tracking of Multi Class Vehicles using Locality Sensitive Histogram.

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.e5295.019521 ◽

2021 ◽

Vol 9 (5) ◽

pp. 259-262

Author(s):

Bhavya Rudraiah ◽

Geetha K S

Keyword(s):

Feature Extraction ◽

Decision Tree ◽

Performance Parameters ◽

Decision Tree Classifier ◽

Multiple Objects ◽

Detection And Tracking ◽

Tree Classifier ◽

Multiple Detection ◽

Cluttered Background ◽

Histogram Feature

Multiple object detection and tracking in a cluttered background is most important in vision-based applications. In this paper, the goal is to develop a classifier that detects and tracks multiple objects thereby ensuring robustness and accuracy. Locality Sensitive Histogram feature extraction is used, which adds contributions from all the pixels in an image. These features extracted are trained using decision tree classifier which performs with an accuracy of 97%. Experimental results demonstrate the objects tracked and detected under different scale and pose variations. Evaluation and comparison of the proposed method with various other techniques is performed using performance parameters. Results depict that the proposed technique outperforms with increased accuracy and is the top performer

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