Real-Time Object Detection with Simultaneous Denoising using Low-Rank and Total Variation Models

Abdulghafoor, Nuha H.; Abdullah, Hadeel N.

doi:10.1109/hora49412.2020.9152863

Cited by 5 publications

(2 citation statements)

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“…model to remove many types of noise simultaneously. The adaptive modeling technique may automatically eliminate noise and detect the foreground, whether actual or internet films are utilized as in [14]. Since its inception, evaluating multi-object tracking (MOT) systems has been challenging.…”

Section: Related Workmentioning

confidence: 99%

Improve Multi-Object Detection and Tracking for an Automated Traffic Surveillance System

2023

IJCCCE

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Multi-Object Detection and Tracking (MODT) are essential in many application fields. Still, many enhancements in the speed of detection and tracking were required to overcome the challenges during implementation. This paper presents a new algorithm system for (MODT) to improve the execution time to be robust in real-time applications. A background subtraction detection algorithm with a Kalman filter was used to track and predict the object position and speed parameters. To improve the processing time, its needs to reduce some frames in a way that does not affect the detection accuracy too much and instead use the prediction and the estimated value obtained based on the Kalman filter for the tracked object. This work uses a single video camera to show how effectively to compute and detect multiple objects concurrently; it is applied for daytime preprocessing in an automated traffic surveillance system. Preliminary testing findings show that the suggested algorithm for this vehicle monitoring system is feasible and effective. It illustrates that using the suggested algorithm with a single video camera can simultaneously watch, detect, and track several vehicles and improve execution time. Simulation results on the built system demonstrate that the proposed system reduced the execution time to approximately 41.5% compared to the standard background subtraction algorithm. Results indicate the proposed algorithm has an approximate error for the position and speed of detected and tracked objects compared with the standard background subtraction algorithm. Index Terms— multi-object detection, tracking, background subtraction, Kalman filter, morphological.

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Section: Related Workmentioning

confidence: 99%

Improve Multi-Object Detection and Tracking for an Automated Traffic Surveillance System

2023

IJCCCE

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“…With the development of defensive land-based weapons, it has been challenging to identify these threats. Therefore, to deal with these increasing difficulties, the researchers made a combat model that gradually moved away from the standard algorithms [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Obstacle Avoidance and Path Planning for UAV Using Laguerre Polynomial

2022

IJIES

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Recently, path planning algorithms have been one of the primary and important functions of unmanned aerial vehicles (UAVs). Path planning algorithms in UAVs focused on path length, average path length, computation time, and standard deviation from the mean path length. In spite of this, it faced many difficulties and problems, such as many obstacles, path segmentation, and the increasing number of obstacles and paths in urban environments. This work proposes polynomial functions for path planning and obstacle avoidance. Since it enables us to plan the path in static internal environments, it enables us to plan the path quickly and with less computing time because it does not require high memory and does not require pre-compute of the path. Instead, the route is plotted in real time, Where the appropriate equation is entered into the program, so that the vehicle follows the curve of the entered equation. An accurate data set and metrics were used to measure the efficiency of the proposed method. The experimental results showed a clear improvement in the work of the polynomial function on A*, PSO and genetic algorithms, as this improvement appears very clearly when compared to the computing time, which was reduced by 15% in the method of polynomial functions where the path calculation took only parts of The second, as well as the path length was halved in the polynomial method as the results showed, which reduces the time of battery and memory consumption, the cost of calculating the path and the time to reach the goal.

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