Accurate Detection Of Moving Objects Research Articles

Substraksi Latar Menggunakan Nilai Mean Untuk Klasifikasi Kendaraan Bergerak Berbasis Deep Learning

Moving object detection systems have been widely used in everyday life. Currently, research in the field of background subtraction is still being carried out to achieve maximum accuracy results. This study aims to model the background subtraction of an image using the mean value with the concept of non-overlapping block. Furthermore, the background abstraction results will be used in deep learning-based moving object detection. Specifically, the input image will be divided into several blocks, then the mean value of each block will be calculated to later produce a binary block (binary map). The binary blocks that have been generated will be used as input for background modeling. The background model aims to separate moving objects from the background in the input image. The resulting moving object (object localization) will be sent to the object classification stage using deep learning. The dataset used in this study is CDNet 2014. The results of the study were able to produce a more accurate moving object detection system. Quantitative tests carried out resulted in an accuracy of above 90%.

DFC-D: A dynamic weight-based multiple features combination for real-time moving object detection

Real-time moving object detection is an emerging method in Industry 5.0, that is applied in video surveillance, video coding, human-computer interaction, IoT, robotics, smart home, smart environment, edge and fog computing, cloud computing, and so on. One of the main issues is accurate moving object detection in real-time in a video with challenging background scenes. Numerous existing approaches used multiple features simultaneously to address the problem but did not consider any adaptive/dynamic weight factor to combine these feature spaces. Being inspired by these observations, we propose a background subtraction-based real-time moving object detection method, called DFC-D. This proposal determines an adaptive/dynamic weight factor to provide a weighted fusion of non-smoothing color/gray intensity and non-smoothing gradient magnitude. Moreover, the color-gradient background difference and segmentation noise are employed to modify thresholds and background samples. Our proposed solution achieves the best trade-off between detection accuracy and algorithmic complexity on the benchmark datasets while comparing with the state-of-the-art approaches.

Annotation and Benchmarking of a Video Dataset under Degraded Complex Atmospheric Conditions and Its Visibility Enhancement Analysis for Moving Object Detection

Detection of moving objects in outdoor environments is an extremely researched topic. However, studies on moving object detection in complex atmospheric/weather conditions are limited, primarily because of the absence of any relevant benchmark dataset. To address this disparity, we introduce a novel benchmark video dataset entitled “Extended Tripura University Video Dataset (E-TUVD)” which is a diverse dataset of complex atmospheric/weather conditions. Currently, E-TUVD is the largest video dataset for moving object detection under degraded atmospheric/weather conditions. The dataset comprises 147 video clips spanning 1-5 minutes in duration of each video clips. Because of the requirement of evaluating any object detection model, this study emphasizes on generation of ground-truth images of salient moving objects on E-TUVD. Using this dataset, we assessed the performance of several state-of-the-art algorithms, considering both the ability to detect moving objects and visibility enhancement under such complex conditions. The method with the best performance was used to investigate the effectiveness of visibility enhancement of atmospheric/weather degraded image sequences for accurate moving object detection. Results and analysis reveal that effective enhancement can significantly improve the ability of detection algorithms under degraded atmospheric/weather conditions to resemble the true properties of moving objects in terms of pixel oriented binary masks.

An Automated Unified Framework for Video Deraining and Simultaneous Moving Object Detection in Surveillance Environments

In many instances, outdoor surveillance systems suffer from atrocious weather conditions such as rain, since images or videos captured by such vision systems in rainy days may undergo severe visual dilapidations. This can cause a glitch in those algorithms which are further used for object detection and tracking. Therefore, an ancillary video processing algorithm namely, video deraining is necessary prior to the implementation of object detection and tracking. This indicates the requirement of a time-consuming and complicated two-step process for the detection of moving objects in a rainy environment. This paper proposes an automated single-stage formulation for the conventional three-stage procedure such as rain steak removal, original data recovery and moving object detection as simultaneous operation in the tensor framework. The brilliance of this work is confined in the efficient formulation of an operator termed as Slice Rotational Total Variation (SRTV), to unify the different rain patterns into a common pattern so that any form of rain pattern can be effectively removed from the rainy data in a visually appealing manner by preserving the important details of the data. In this paper, SRTV regularization and tensor low-rank minimization are utilized for the effective deraining as well as efficient retrieval of clean background. Besides, $l_{1}$ norm and Tensor Total Variation (TTV) regularizers together with SRTV regularizer are employed for the faithful detection of derained moving objects. The experimental results show that the proposed method outperforms the state of the art methods in terms of deraining capability and accurate moving object detection.

Real Time Pedestrian Detection and Tracking for Driver Assistance Systems

In Autonomous driving technology detecting pedestrians and vehicles should be fast and efficient in order to avoid accidents. Pedestrian detection and tracking is challenging for complex real world scenes. In proposed system Kalman filter has been used to detect and track the pedestrians. From three frames initially eigen object is computed in video sequences for detection of moving objects, then shape information is used to classify humans and other objects. Moreover with the help of continues multiple frames occlusion between objects get calculated. In the proposed system an application is developed which gives automatic warning in case of doubtful activities performed by pedestrian of zone monitoring which can be used in various domains like defence and traffic monitoring. Proposed algorithm gives accurate moving object detection and advanced sensors are used to detect human movements ahead and alert the driver by using buzzer, result does not affect by body pose of individual.

Compressed Domain Moving Object Detection Based on CRF

This paper aims to present a novel accurate moving object detection method based on the conditional random field (CRF) for high efficiency video coding/H.265 compressed domain video sequences. For each block, the number of consumed bits, motion vectors (MVs), and partitioning modes for a given block is extracted from the compressed bitstream. After removing outlier MVs, compensating MVs are assigned to the I-blocks based on their neighboring blocks. The information, such as MV, partitioning mode, and bit consumption, is used in the potential functions of a CRF model which is updated for every frame to detect the objects. Then, a number of standard test video sequences are used to verify the performance of the model. The results indicated that the model can offer a precision, that is more than 90% on average for the video sequences. The proposed method offers a 1.8 speedup, compared to the latest works in the compressed domain without losing the objects in the I-frames.

Background Subtraction Berbasis Self Organizing Map Untuk Deteksi Objek Bergerak

First part in automatic video analisys is moving object detection. An accurate moving object detection is needed indeed to next step process of automatic video analisys like tracking object detected adn then analyze of detected object. Background Subtraction is a common approach in moving object detection. The common problems in background subtraction are illumination changes, object shadow, and dynamic background like waving tree. Self organizing Maps algorithm apllied in background Subtraction to handles these common problems. Median filtering and morphological operation added after background Subtraction procces in conjunction to increase and produce accurate moving object detection. Apllied SOM, median filtering, and morphological operation in background subtraction increasing object detection accuraccy with value of MSE in 1463,73 and PSNR in 17,035 compare with alpha based background Subtraction where 4268,50 for MSE and 12,018 for PSNR.

Surveillance video summarisation by jointly applying moving object detection and tracking

With the growth of massive storage of surveillance video data, it has become imperative to design efficient tools for video content browsing and management. This paper describes an integrative approach for surveillance video summarisation that jointly apply moving object detection and tracking. In the proposed scheme, moving objects are first detected and tracked. The static summarisation is generated to contain some key frames which provide details of the moving objects. The main advantages of our approach include the preservation of important information and economic computational cost. The high performance background modelling with Gaussian mixture model, together with the multi-scale morphological processing, brings together a highly accurate moving object detection tool. The proposed matching criterions for Kalman filtering enhances the tracking accuracy. We experimented with highway surveillance videos and outdoor surveillance videos, demonstrating satisfactory performances.

Highly accurate moving object detection in variable bit rate video-based traffic monitoring systems.

Automated motion detection, which segments moving objects from video streams, is the key technology of intelligent transportation systems for traffic management. Traffic surveillance systems use video communication over real-world networks with limited bandwidth, which frequently suffers because of either network congestion or unstable bandwidth. Evidence supporting these problems abounds in publications about wireless video communication. Thus, to effectively perform the arduous task of motion detection over a network with unstable bandwidth, a process by which bit-rate is allocated to match the available network bandwidth is necessitated. This process is accomplished by the rate control scheme. This paper presents a new motion detection approach that is based on the cerebellar-model-articulation-controller (CMAC) through artificial neural networks to completely and accurately detect moving objects in both high and low bit-rate video streams. The proposed approach is consisted of a probabilistic background generation (PBG) module and a moving object detection (MOD) module. To ensure that the properties of variable bit-rate video streams are accommodated, the proposed PBG module effectively produces a probabilistic background model through an unsupervised learning process over variable bit-rate video streams. Next, the MOD module, which is based on the CMAC network, completely and accurately detects moving objects in both low and high bit-rate video streams by implementing two procedures: 1) a block selection procedure and 2) an object detection procedure. The detection results show that our proposed approach is capable of performing with higher efficacy when compared with the results produced by other state-of-the-art approaches in variable bit-rate video streams over real-world limited bandwidth networks. Both qualitative and quantitative evaluations support this claim; for instance, the proposed approach achieves Similarity and F1 accuracy rates that are 76.40% and 84.37% higher than those of existing approaches, respectively.

Comparative Study on Moving Object Detection Method of Video Surveillance System in China High-Speed Railway Transport Hub

To detect moving objects from a video sequence is a fundamental and critical task in many computer vision applications. For China high-speed railway transport hub video surveillance system, we need a stable, fast and accurate moving object detection method to promptly find the congestion of passenger flow and other dangerous in hub. Through the comparative study on moving object detection, we find the average/median filtering is the most appropriate method for China high-speed railway transport hub video surveillance.