scholarly journals Abandoned Object Detection in Video-Surveillance: Survey and Comparison

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4290 ◽  
Author(s):  
Elena Luna ◽  
Juan San Miguel ◽  
Diego Ortego ◽  
José Martínez
Keyword(s):  
Object Detection ◽  
Video Surveillance ◽  
Moving Objects ◽  
Automatic Monitoring ◽  
Experimental Comparison ◽  
Public And Private ◽  
Extensive Evaluation ◽  
Abandoned Object ◽  
Configuration System ◽  
The Impact

During the last few years, abandoned object detection has emerged as a hot topic in the video-surveillance community. As a consequence, a myriad of systems has been proposed for automatic monitoring of public and private places, while addressing several challenges affecting detection performance. Due to the complexity of these systems, researchers often address independently the different analysis stages such as foreground segmentation, stationary object detection, and abandonment validation. Despite the improvements achieved for each stage, the advances are rarely applied to the full pipeline, and therefore, the impact of each stage of improvement on the overall system performance has not been studied. In this paper, we formalize the framework employed by systems for abandoned object detection and provide an extensive review of state-of-the-art approaches for each stage. We also build a multi-configuration system allowing one to select a range of alternatives for each stage with the objective of determining the combination achieving the best performance. This multi-configuration is made available online to the research community. We perform an extensive evaluation by gathering a heterogeneous dataset from existing data. Such a dataset allows considering multiple and different scenarios, whereas presenting various challenges such as illumination changes, shadows, and a high density of moving objects, unlike existing literature focusing on a few sequences. The experimental results identify the most effective configurations and highlight design choices favoring robustness to errors. Moreover, we validated such an optimal configuration on additional datasets not previously considered. We conclude the paper by discussing open research challenges arising from the experimental comparison.

scholarly journals 3D Semantic VSLAM of Indoor Environment Based on Mask Scoring RCNN

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Chongben Tao ◽  
Yufeng Jin ◽  
Feng Cao ◽  
Zufeng Zhang ◽  
Chunguang Li ◽  
...  
Keyword(s):  
Indoor Environment ◽  
Semantic Information ◽  
Moving Objects ◽  
Experimental Comparison ◽  
Indoor Environments ◽  
Feature Points ◽  
Current Frame ◽  
Semantic Map ◽  
Key Frame ◽  
The Impact

In view of existing Visual SLAM (VSLAM) algorithms when constructing semantic map of indoor environment, there are problems with low accuracy and low label classification accuracy when feature points are sparse. This paper proposed a 3D semantic VSLAM algorithm called BMASK-RCNN based on Mask Scoring RCNN. Firstly, feature points of images are extracted by Binary Robust Invariant Scalable Keypoints (BRISK) algorithm. Secondly, map points of reference key frame are projected to current frame for feature matching and pose estimation, and an inverse depth filter is used to estimate scene depth of created key frame to obtain camera pose changes. In order to achieve object detection and semantic segmentation for both static objects and dynamic objects in indoor environments and then construct dense 3D semantic map with VSLAM algorithm, a Mask Scoring RCNN is used to adjust its structure partially, where a TUM RGB-D SLAM dataset for transfer learning is employed. Semantic information of independent targets in scenes provides semantic information including categories, which not only provides high accuracy of localization but also realizes the probability update of semantic estimation by marking movable objects, thereby reducing the impact of moving objects on real-time mapping. Through simulation and actual experimental comparison with other three algorithms, results show the proposed algorithm has better robustness, and semantic information used in 3D semantic mapping can be accurately obtained.

scholarly journals Features-Based Moving Objects Tracking for Smart Video Surveillances: A Review

2018 ◽  
Vol 27 (02) ◽  
pp. 1830001 ◽  
Author(s):  
Nor Nadirah Abdul Aziz ◽  
Yasir Mohd Mustafah ◽  
Amelia Wong Azman ◽  
Amir Akramin Shafie ◽  
Muhammad Izad Yusoff ◽  
...  
Keyword(s):  
Object Detection ◽  
Video Surveillance ◽  
Surveillance System ◽  
Moving Objects ◽  
Visual Surveillance ◽  
Surveillance Systems ◽  
Objects Tracking ◽  
Object Appearance ◽  
Human Operators ◽  
Active Research

Video surveillance is one of the most active research topics in the computer vision due to the increasing need for security. Although surveillance systems are getting cheaper, the cost of having human operators to monitor the video feed can be very expensive and inefficient. To overcome this problem, the automated visual surveillance system can be used to detect any suspicious activities that require immediate action. The framework of a video surveillance system encompasses a large scope in machine vision, they are background modelling, object detection, moving objects classification, tracking, motion analysis, and require fusion of information from the camera networks. This paper reviews recent techniques used by researchers for detection of moving object detection and tracking in order to solve many surveillance problems. The features and algorithms used for modelling the object appearance and tracking multiple objects in outdoor and indoor environment are also reviewed in this paper. This paper summarizes the recent works done by previous researchers in moving objects tracking for single camera view and multiple cameras views. Nevertheless, despite of the recent progress in surveillance technologies, there still are challenges that need to be solved before the system can come out with a reliable automated video surveillance.

scholarly journals Embedded Video Surveillance Based Moving Object Detection and Tracking

2019 ◽  
Vol 8 (3) ◽  
pp. 5740-5745
Keyword(s):  
Object Detection ◽  
Optical Flow ◽  
Video Surveillance ◽  
Intelligent Systems ◽  
Moving Objects ◽  
Moving Object Detection ◽  
Moving Object ◽  
Superior Performance ◽  
Detection And Tracking ◽  
Gradient Based

Background reckoning and the foreground, play prominent roles in the tasks of visual detection and tracking of objects. Moving Object Detection has been widely used in sundry discipline such as intelligent systems, security systems, video monitoring systems, banking places, provisionary systems, and so on. In this paper proposes moving objects detection and tracking method based on Embedded Video Surveillance. The method is based on using lines computed by a gradient-based optical flow and an edge detector gradient-based optical flow and edges are well matched for accurate computation of velocity, not much attention is paid to creating systems for tracking objects using this feature. The proposed method is compared with a recent work, proving its superior performance and when we want to represent high quality videos and images with, lower bit rate, and also suitable for real-world live video applications. This method reduces influences of foreground objects to the background model. The simulation results show that the background image can be obtained precisely and the moving objects recognition is achieved effectively

scholarly journals A Fuzzy rule- based Abandoned Object Detection using Image Fusion for Intelligent Video Surveillance Systems

2021 ◽  
Vol 12 (3) ◽  
pp. 3694-3702
Author(s):  
Preetha K G Et.al
Keyword(s):  
Object Detection ◽  
Video Surveillance ◽  
Fuzzy Rule ◽  
Super Resolution ◽  
Threat Assessment ◽  
Video Enhancement ◽  
Shopping Malls ◽  
Railway Stations ◽  
Rule Based ◽  
Abandoned Object

Abandoned object/luggage is a major threat in all public scenes like hospitals, railway stations, airports and shopping malls.  Abandoned luggage may contain explosive, biological warfare or smuggled goods. Abandoned object detection is the process to identify the unattended strange object within a specific time.  It is also crucial to identify the person who has abandoned the luggage in the scene. Video surveillance is one of the essential techniques for automatic video analysis to extract crucial information or relevant scenes. The main objectives of this work is the automatic detection of abandoned objects and related persons in public areas like airports, railway stations, shopping malls etc.  Video enhancement techniques like residual dense networks are adopted to improve the quality of the image before applying it to detect the abandoned objects and related humans. The scenario of abandoned objects and related humans are identified through distance differencing methods.  Once the scene is identified, the method is capable of producing alert messages or alarms in real-time through automated means.  A fuzzy rule based threat assessment module is also incorporated in this work which reduces the false alarm rate. The related person is identified through reconstruction of the face through super-resolution techniques. Experiments are found to be appreciable in terms of the metrics in video enhancement, detection, fuzzification and face super-resolution.

An effective motion object detection using adaptive background modeling mechanism in video surveillance system

2021 ◽  
pp. 1-13
Author(s):  
SivaNagiReddy Kalli ◽  
T. Suresh ◽  
A. Prasanth ◽  
T. Muthumanickam ◽  
K. Mohanram
Keyword(s):  
Object Detection ◽  
Video Surveillance ◽  
Surveillance System ◽  
Moving Objects ◽  
Detection System ◽  
Research Work ◽  
Traffic Monitoring ◽  
Background Modeling ◽  
Video Surveillance System ◽  
Fuzzy C Means

Automatic moving object detection has gained increased research interest due to its widespread applications like security provision, traffic monitoring, and various types of anomalies detection, etc. In the video surveillance system, the video is processed for the detection of motion objects in a step-by-step process. However, the detection has become complex and less effective due to various complex constraints. To obtain an effective performance in the detection of motion objects, this research work focuses to develop an automatic motion object detection system based on the statistical properties of video and supervised learning. In this paper, a novel Background Modeling mechanism is proposed with the help of a Biased Illumination Field Fuzzy C-means algorithm to detect the moving objects more accurately. Here, the non-stationary pixels are separated from stationary pixels through the Background Subtraction. Afterward, the Biased Illumination Field Fuzzy C-means approach has accomplished to improve the segmentation accuracy through clustering under noise and varying illumination conditions. The performance of the proposed algorithm compared with conventional methods in terms of accuracy, precision, recall, and F- measure.

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