scholarly journals Fusing Multimodal Video Data for Detecting Moving Objects/Targets in Challenging Indoor and Outdoor Scenes

2019 ◽  
Vol 11 (4) ◽  
pp. 446 ◽  
Author(s):  
Zacharias Kandylakis ◽  
Konstantinos Vasili ◽  
Konstantinos Karantzalos
Keyword(s):  
Object Detection ◽  
Moving Objects ◽  
Local Coordinate System ◽  
Image Plane ◽  
Hyperspectral Data ◽  
Video Data ◽  
Outdoor Scenes ◽  
Indoor Scenes ◽  
Video Sensors ◽  
Indoor And Outdoor

Single sensor systems and standard optical—usually RGB CCTV video cameras—fail to provide adequate observations, or the amount of spectral information required to build rich, expressive, discriminative features for object detection and tracking tasks in challenging outdoor and indoor scenes under various environmental/illumination conditions. Towards this direction, we have designed a multisensor system based on thermal, shortwave infrared, and hyperspectral video sensors and propose a processing pipeline able to perform in real-time object detection tasks despite the huge amount of the concurrently acquired video streams. In particular, in order to avoid the computationally intensive coregistration of the hyperspectral data with other imaging modalities, the initially detected targets are projected through a local coordinate system on the hypercube image plane. Regarding the object detection, a detector-agnostic procedure has been developed, integrating both unsupervised (background subtraction) and supervised (deep learning convolutional neural networks) techniques for validation purposes. The detected and verified targets are extracted through the fusion and data association steps based on temporal spectral signatures of both target and background. The quite promising experimental results in challenging indoor and outdoor scenes indicated the robust and efficient performance of the developed methodology under different conditions like fog, smoke, and illumination changes.

scholarly journals Optimized Multi-layer Machine Learning Technique for Movement Detection and Tracking in Video Data Stream

2019 ◽  
Vol 8 (10) ◽  
pp. 1151-1159
Keyword(s):  
Object Detection ◽  
Moving Objects ◽  
Error Rates ◽  
Moving Object Detection ◽  
Video Data ◽  
Moving Object ◽  
Detection Accuracy ◽  
Efficient System ◽  
Detection And Tracking ◽  
The Given

With the advent in technology, security and authentication has become the main aspect in computer vision approach. Moving object detection is an efficient system with the goal of preserving the perceptible and principal source in a group. Surveillance is one of the most crucial requirements and carried out to monitor various kinds of activities. The detection and tracking of moving objects are the fundamental concept that comes under the surveillance systems. Moving object recognition is challenging approach in the field of digital image processing. Moving object detection relies on few of the applications which are Human Machine Interaction (HMI), Safety and video Surveillance, Augmented Realism, Transportation Monitoring on Roads, Medical Imaging etc. The main goal of this research is the detection and tracking moving object. In proposed approach, based on the pre-processing method in which there is extraction of the frames with reduction of dimension. It applies the morphological methods to clean the foreground image in the moving objects and texture based feature extract using component analysis method. After that, design a novel method which is optimized multilayer perceptron neural network. It used the optimized layers based on the Pbest and Gbest particle position in the objects. It finds the fitness values which is binary values (x_update, y_update) of swarm or object positions. Method and output achieved final frame creation of the moving objects in the video using BLOB ANALYSER In this research , an application is designed using MATLAB VERSION 2016a In activation function to re-filter the given input and final output calculated with the help of pre-defined sigmoid. In proposed methods to find the clear detection and tracking in the given dataset MOT, FOOTBALL, INDOOR and OUTDOOR datasets. To improve the detection accuracy rate, recall rate and reduce the error rates, False Positive and Negative rate and compare with the various classifiers such as KNN, MLPNN and J48 decision Tree.

scholarly journals 3D Object Detection Using Scale Invariant and Feature Reweighting Networks

2019 ◽  
Vol 33 ◽  
pp. 9267-9274 ◽  
Author(s):  
Xin Zhao ◽  
Zhe Liu ◽  
Ruolan Hu ◽  
Kaiqi Huang
Keyword(s):  
Object Detection ◽  
Network Architecture ◽  
Point Clouds ◽  
Scale Invariant ◽  
3D Object ◽  
Outdoor Scenes ◽  
Indoor Scenes ◽  
Bounding Boxes ◽  
The One ◽  
3D Object Detection

3D object detection plays an important role in a large number of real-world applications. It requires us to estimate the localizations and the orientations of 3D objects in real scenes. In this paper, we present a new network architecture which focuses on utilizing the front view images and frustum point clouds to generate 3D detection results. On the one hand, a PointSIFT module is utilized to improve the performance of 3D segmentation. It can capture the information from different orientations in space and the robustness to different scale shapes. On the other hand, our network obtains the useful features and suppresses the features with less information by a SENet module. This module reweights channel features and estimates the 3D bounding boxes more effectively. Our method is evaluated on both KITTI dataset for outdoor scenes and SUN-RGBD dataset for indoor scenes. The experimental results illustrate that our method achieves better performance than the state-of-the-art methods especially when point clouds are highly sparse.

scholarly journals Insects and associated arthropods analyzed during medicolegal death investigations in Harris County, Texas, USA: January 2013-April 2016

2016 ◽  
Author(s):  
Michelle R. Sanford
Keyword(s):  
Forensic Entomology ◽  
Gulf Coast ◽  
Forensic Sciences ◽  
Full Time ◽  
Harris County ◽  
Species List ◽  
Manner Of Death ◽  
Outdoor Scenes ◽  
Indoor Scenes ◽  
Indoor And Outdoor

ABSTRACTThe application of insect and arthropod information to medicolegal death investigations is one of the more exacting applications of entomology. Historically limited to homicide investigations, the integration of full time forensic entomology services to the medical examiner’s office in Harris County has opened up the opportunity to apply entomology to a wide variety of manner of death classifications and types of scenes to make observations on a number of different geographical and species-level trends in Harris County, Texas, USA. In this study, a retrospective analysis was made of 203 forensic entomology cases analyzed during the course of medicolegal death investigations performed by the Harris County Institute of Forensic Sciences in Houston, TX, USA from January 2013 through April 2016. These cases included all manner of death classifications, stages of decomposition and a variety of different scene types that were classified into decedents transported from the hospital (typically associated with myiasis or sting allergy; 3.0%), outdoor scenes (32.0%) or indoor scenes (65.0%). Ambient scene air temperature at the time scene investigation was the only significantly different factor observed between indoor and outdoor scenes with average indoor scene temperature being slightly cooler (25.2°C) than that observed outdoors (28.0°C). Relative humidity was not found to be significantly different between scene types. Most of the indoor scenes were classified as natural (43.3%) whereas most of the outdoor scenes were classified as homicides (12.3%). All other manner of death classifications came from both indoor and outdoor scenes. Several species were found to be significantly associated with indoor scenes as indicated by a binomial test, including Blaesoxipha plinthopyga (Sarcophagidae), all Sarcophagidae including B. plinthopyga, Megaselia scalaris (Phoridae), Synthesiomyia nudiseta (Muscidae) and Lucilia cuprina (Calliphoridae). The only species that was a significant indicator of an outdoor scene was Lucilia eximia (Calliphoridae). All other insect species that were collected in five or more cases were collected from both indoor and outdoor scenes. A species list with month of collection and basic scene characteristics with the length of the estimated time of colonization is also presented. The data presented here provide valuable casework related species data for Harris County, TX and nearby areas on the Gulf Coast that can be used to compare to other climate regions with other species assemblages and to assist in identifying new species introductions to the area. This study also highlights the importance of potential sources of uncertainty in preparation and interpretation of forensic entomology reports from different scene types.

THE USE OF OPTICAL FLOW FOR THE AUTONOMOUS NAVIGATION

1992 ◽  
Vol 03 (supp01) ◽  
pp. 121-137 ◽  
Author(s):  
Antonio Malisia ◽  
Andrea Baghino ◽  
Marco Campani ◽  
Marco Straforini ◽  
Vincent Torre
Keyword(s):  
Optical Flow ◽  
Autonomous Navigation ◽  
Moving Vehicle ◽  
Mobile Vehicle ◽  
Outdoor Scenes ◽  
Outdoor Environments ◽  
Indoor Scenes ◽  
Indoor And Outdoor Environments ◽  
Vision Module ◽  
Indoor And Outdoor

Insects and a lot of other animals use the optical flow to control the direction of their motion and to avoid obstacles. This paper describes experiments suggesting the possible use of the optical flow for the navigation of a robot moving in indoor and outdoor environments. In indoor scenes, such as corridors, offices and laboratories, the optical flow is used to detect and localize obstacles. These routines are based on the computation of a reduced optical flow. Almost real time performance was obtained with standard workstations, such as SUN 3 or SUN Sparcstation 1. The mobile vehicle is usually able to avoid large obstacles such as a chair or a human, but it is not able to avoid thin obstacles such as a rod or a bar. The avoidance performances of the proposed algorithm critically depend on the feedback loop between the vision module and the motor system. In outdoor scenes the optical flow can be used to understand the egomotion, that is to obtain information on the absolute velocity of the moving vehicle. The optical flow is corrected for shocks and vibration present during image acquisition. Regions of the image are extracted, where the optical flow is reliable, and the information on egomotion is recovered from the optical flow here obtained. These results suggest that the optical flow can be successfully used by biological and artificial systems for controlling their motion and for avoiding obstacles.

scholarly journals MULTIMODAL DATA FUSION FOR EFFECTIVE SURVEILLANCE OF CRITICAL INFRASTRUCTURE

2017 ◽  
Vol XLII-3/W3 ◽  
pp. 87-93 ◽  
Author(s):  
Z. Kandylakis ◽  
K. Karantzalos ◽  
A. Doulamis ◽  
L. Karagiannidis
Keyword(s):  
Critical Infrastructure ◽  
Local Coordinate System ◽  
Data Association ◽  
Image Plane ◽  
Surveillance Systems ◽  
Moving Targets ◽  
Dynamic Background ◽  
Multisensor Systems ◽  
Video Sensors ◽  
Shortwave Infrared

Monitoring critical infrastructures, especially those that are covering wide-zones, is of fundamental importance and priority for modern surveillance systems. The concurrent exploitation of multisensor systems, can offer additional capabilities, on day and night acquisitions and different environmental/illumination conditions. Towards this direction, we have designed a multi-sensor system based on thermal, shortwave infrared and hyperspectral video sensors. Based on advanced registration, dynamic background modelling and data association techniques, possible moving targets are detected on the thermal and shortwave infrared modalities. In order to avoid the computational intensive co-registration with the hyperspectral video streams, the detected targets are projected through a local coordinate system on the hypercube image plane. The final detected and verified targets are extracted through fusion and data association, based on temporal spectral signatures and target/background statistics. The developed multisensor system for the surveillance of critical infrastructure has been validated for monitoring wide-zones against different conditions showcasing abilities for detecting and tracking moving targets through fog and smoke.

scholarly journals CONFIGURATION AND SIMULATION TOOL FOR 360-DEGREE STEREO CAMERA RIG

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 793-798
Author(s):  
O. Hasler ◽  
B. Loesch ◽  
S. Blaser ◽  
S. Nebiker
Keyword(s):  
Focal Length ◽  
Evaluation Process ◽  
Camera System ◽  
Stereo Camera ◽  
Multiple Parameters ◽  
Outdoor Scenes ◽  
Indoor Scenes ◽  
User Friendly ◽  
Intrinsic And Extrinsic Parameters ◽  
Indoor And Outdoor

<p><strong>Abstract.</strong> The demand for capturing outdoor and indoor scenes is rising with the digitalization trend in the construction industry. An efficient solution for capturing these environments is mobile mapping. Image-based systems with 360&amp;deg; panoramic coverage allow a rapid data acquisition and can be made user-friendly accessible when hosted in a cloud-based 3D geoinformation service. The design of such a 360° stereo camera system is challenging since multiple parameters like focal length, stereo base length and environmental restrictions such as narrow corridors are influencing each other. Therefore, this paper presents a toolset, which helps configuring and evaluating such a panorama stereo camera rig. The first tool is used to determine, from which distance on 360&amp;deg; stereo coverage depending on the parametrization of the rig is achieved. The second tool can be used to capture images with the parametrized camera rig in different virtual indoor and outdoor scenes. The last tool supports stitching the captured images together in respect of the intrinsic and extrinsic parameters from the configuration tool. This toolset radically simplifies the evaluation process of a 360&amp;deg; stereo camera configuration and decreases the number of physical MMS prototypes.</p>

Anfis Classifier Based Moving Object Detection and Segmentation in Indoor and Outdoor Environments

2019 ◽  
Vol 14 (1) ◽  
pp. 21-30
Author(s):  
A. Shyamala ◽  
S. Selvaperumal ◽  
G. Prabhakar
Keyword(s):  
Object Detection ◽  
Moving Objects ◽  
Moving Object Detection ◽  
Moving Object ◽  
Video Sequences ◽  
Detection Time ◽  
Outdoor Environments ◽  
Anfis Classifier ◽  
Indoor And Outdoor Environments ◽  
Indoor And Outdoor

Background: Moving object detection in dynamic environment video is more complex than the static environment videos. In this paper, moving objects in video sequences are detected and segmented using feature extraction based Adaptive Neuro-Fuzzy Inference System (ANFIS) classifier approach. The proposed moving object detection methodology is tested on different video sequences in both indoor and outdoor environments. Methods: This proposed methodology consists of background subtraction and classification modules. The absolute difference image is constructed in background subtraction module. The features are extracted from this difference image and these extracted features are trained and classified using ANFIS classification module. Results: The proposed moving object detection methodology is analyzed in terms of Accuracy, Recall, Average Accuracy, Precision and F-measure. The proposed moving object segmentation methodology is executed on different Central Processing Unit (CPU) processor as 1.8 GHz and 2.4 GHz for evaluating the performance during moving object segmentation. At present, some moving object detection systems used 1.8 GHz CPU processor. Recently, many systems for moving object detection are using 2.4 GHz CPU processor. Hence, CPU processors 1.8 GHz and 2.4 GHz are used in this paper for detecting the moving objects in video sequences. Table 1 shows the performance evaluation of proposed moving object detection on CPU processor 1.8 GHz (100 sequence). Table 2 shows the performance evaluation of proposed moving object detection on CPU processor 2.8 GHz (100 sequence). The average moving object detection time on CPU processor 1.8 GHz for fountain sequence is 62.5 seconds, for airport sequence is 64.7 seconds, for meeting room sequence is 71.6 seconds and for Lobby sequence is 73.5 seconds, respectively, as depicted in Table 3. The average elapsed time for moving object detection on 100 sequences is 68.07 seconds. The average moving object detection time on CPU processor 2.4 GHz for fountain sequence is 56.5 seconds, for airport sequence is 54.7 seconds, for meeting room sequence is 65.8 seconds and for Lobby sequence is 67.5 seconds, respectively, as depicted in Table 4. The average elapsed time for moving object detection on 100 sequences is 61.12 seconds. It is very clear from Table 3 and Table 4; the moving object detection time is reduced when the frequency of the CPU processor increases. Conclusion: In this paper, moving object is detected and segmented using ANFIS classifier. The proposed method initially segments the background image and then features are extracted from the threshold image. These features are trained and classified using ANFIS classification method. The proposed moving object detection method is tested on different video sequences which are obtained from different indoor and outdoor environments. The performance of the proposed moving object detection and segmentation methodology is analyzed in terms of Accuracy, Recall, Average Accuracy, Precision and F-measure.

scholarly journals New Approach To Traffic Density Estimation Based On Indoor And Outdoor Scenes From CCTV

2015 ◽  
Vol 40 (2) ◽  
pp. 119-132 ◽  
Author(s):  
Agata Chmielewska ◽  
Marianna Parzych ◽  
Tomasz Marciniak ◽  
Adam Dąbrowski
Keyword(s):  
Moving Objects ◽  
Automatic Generation ◽  
Moving Object ◽  
Traffic Density ◽  
Surveillance Systems ◽  
Video Recordings ◽  
Density Maps ◽  
Outdoor Scenes ◽  
Density Map ◽  
Indoor And Outdoor

Abstract In this paper we present an algorithm for precise estimation of moving objects density (typically people and vehicles) in indoor and outdoor scenes. Automatic generation of the so-called density maps is based on video sequences acquired by surveillance systems. Our approach offers two types of solutions. The first one increments the accumulation table when a moving object is detected in a location of interest, delivering a density map of the presence of moving objects. The second algorithm increments the accumulation table only in cases of detecting a new moving object, resulting in a density map of the count of moving objects. The proposed algorithms were tested with the use of PETS 2009 database and with our own database of long-term video recordings. Finally, results of the density maps visualization and determination of the “busy hours” are presented.

scholarly journals A New RGB-D SLAM Method with Moving Object Detection for Dynamic Indoor Scenes

2019 ◽  
Vol 11 (10) ◽  
pp. 1143 ◽  
Author(s):  
Runzhi Wang ◽  
Wenhui Wan ◽  
Yongkang Wang ◽  
Kaichang Di
Keyword(s):  
Object Detection ◽  
Moving Objects ◽  
Moving Object Detection ◽  
Moving Object ◽  
Geometric Constraints ◽  
The Public ◽  
Localization And Mapping ◽  
Real World Applications ◽  
Indoor Scenes ◽  
Practical Performance

Simultaneous localization and mapping (SLAM) methods based on an RGB-D camera have been studied and used in robot navigation and perception. So far, most such SLAM methods have been applied to a static environment. However, these methods are incapable of avoiding the drift errors caused by moving objects such as pedestrians, which limits their practical performance in real-world applications. In this paper, a new RGB-D SLAM with moving object detection for dynamic indoor scenes is proposed. The proposed detection method for moving objects is based on mathematical models and geometric constraints, and it can be incorporated into the SLAM process as a data filtering process. In order to verify the proposed method, we conducted sufficient experiments on the public TUM RGB-D dataset and a sequence image dataset from our Kinect V1 camera; both were acquired in common dynamic indoor scenes. The detailed experimental results of our improved RGB-D SLAM were summarized and demonstrate its effectiveness in dynamic indoor scenes.

scholarly journals Multiple Object Detection and Tracking using Kalman Filter in an Indoor and Outdoor Scene

2020 ◽  
Vol 8 (5) ◽  
pp. 930-933
Keyword(s):  
Kalman Filter ◽  
Object Detection ◽  
Real Time ◽  
High Performance ◽  
Multiple Objects ◽  
Detection And Tracking ◽  
Outdoor Scenes ◽  
Real Time Applications ◽  
Static Background ◽  
Indoor And Outdoor

Tracking target through sequences of images is fundamental problems in vision. In this paper we converse the motion based kalman filter procedure to track the multiple objects for indoor and outdoor scenes. This is of utmost importance for high-performance real -time applications. The mentioned approach is appropriate for indoor & outdoors scenes with static background & overcomes the problem of non-moving objectives fading into the background. The tracking in proposed turned into solely based totally on movement with the belief that each one items move in a immediately line with continuous speed. The motion primarily based Kaman filter monitoring for more than one objects works correctly but requires the camera to be stationary

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