A Novel Approach to Spatio-Temporal Video Analysis and Retrieval

2009 ◽  
pp. 106-115
Author(s):  
Sameer Singh ◽  
Wei Ren ◽  
Maneesha Singh
Keyword(s):  
Video Analysis ◽  
Novel Approach ◽  
Spatio Temporal

scholarly journals Extracting Stops from Spatio-Temporal Trajectories within Dynamic Contextual Features

2021 ◽  
Vol 13 (2) ◽  
pp. 690
Author(s):  
Tao Wu ◽  
Huiqing Shen ◽  
Jianxin Qin ◽  
Longgang Xiang
Keyword(s):  
Moving Objects ◽  
Major Effect ◽  
Location Based Services ◽  
Trajectory Data ◽  
The Road ◽  
Contextual Features ◽  
Novel Approach ◽  
Gps Trajectories ◽  
Time Distance ◽  
Spatio Temporal

Identifying stops from GPS trajectories is one of the main concerns in the study of moving objects and has a major effect on a wide variety of location-based services and applications. Although the spatial and non-spatial characteristics of trajectories have been widely investigated for the identification of stops, few studies have concentrated on the impacts of the contextual features, which are also connected to the road network and nearby Points of Interest (POIs). In order to obtain more precise stop information from moving objects, this paper proposes and implements a novel approach that represents a spatio-temproal dynamics relationship between stopping behaviors and geospatial elements to detect stops. The relationship between the candidate stops based on the standard time–distance threshold approach and the surrounding environmental elements are integrated in a complex way (the mobility context cube) to extract stop features and precisely derive stops using the classifier classification. The methodology presented is designed to reduce the error rate of detection of stops in the work of trajectory data mining. It turns out that 26 features can contribute to recognizing stop behaviors from trajectory data. Additionally, experiments on a real-world trajectory dataset further demonstrate the effectiveness of the proposed approach in improving the accuracy of identifying stops from trajectories.

VIDEO-SHOT TRANSITION DETECTION: A SPATIO-TEMPORAL SALIENCY APPROACH

2010 ◽  
Vol 24 (08) ◽  
pp. 1295-1309 ◽  
Author(s):  
XIAN WU ◽  
JIANHUANG LAI ◽  
PONG C. YUEN
Keyword(s):  
Error Probability ◽  
Spatial Information ◽  
Saliency Map ◽  
Detection Error ◽  
Novel Approach ◽  
Video Shot ◽  
Recent Method ◽  
Temporal And Spatial ◽  
Spatio Temporal ◽  
Shot Transitions

This paper proposes a novel approach for video-shot transition detection using spatio-temporal saliency. Both temporal and spatial information are combined to generate a saliency map, and features are available based on the change of saliency. Considering the context of shot changes, a statistical detector is constructed to determine all types of shot transitions by the minimization of the detection-error probability simultaneously under the same framework. The evaluation performed on videos of various content types demonstrates that the proposed approach outperforms a more recent method and two publicly available systems, namely VideoAnnex and VCM.

scholarly journals A Novel Approach for the Detection of Developing Thunderstorm Cells

2019 ◽  
Vol 11 (4) ◽  
pp. 443 ◽  
Author(s):  
Richard Müller ◽  
Stéphane Haussler ◽  
Matthias Jerg ◽  
Dirk Heizenreder
Keyword(s):  
Water Vapor ◽  
Main Idea ◽  
Probability Of Detection ◽  
Base Case ◽  
Convective Clouds ◽  
Critical Success ◽  
Novel Approach ◽  
Forecast Lead Time ◽  
Spatio Temporal ◽  
Atmospheric Motion Vectors

This study presents a novel approach for the early detection of developing thunderstorms. To date, methods for the detection of developing thunderstorms have usually relied on accurate Atmospheric Motion Vectors (AMVs) for the estimation of the cooling rates of convective clouds, which correspond to the updraft strengths of the cloud objects. In this study, we present a method for the estimation of the updraft strength that does not rely on AMVs. The updraft strength is derived directly from the satellite observations in the SEVIRI water vapor channels. For this purpose, the absolute value of the vector product of spatio-temporal gradients of the SEVIRI water vapor channels is calculated for each satellite pixel, referred to as Normalized Updraft Strength (NUS). The main idea of the concept is that vertical updraft leads to NUS values significantly above zero, whereas horizontal cloud movement leads to NUS values close to zero. Thus, NUS is a measure of the strength of the vertical updraft and can be applied to distinguish between advection and convection. The performance of the method has been investigated for two summer periods in 2016 and 2017 by validation with lightning data. Values of the Critical Success Index (CSI) of about 66% for 2016 and 60% for 2017 demonstrate the good performance of the method. The Probability of Detection (POD) values for the base case are 81.8% for 2016 and 89.2% for 2017, respectively. The corresponding False Alarm Ratio (FAR) values are 22.6% (2016) and 36.4% (2017), respectively. In summary, the method has the potential to reduce forecast lead time significantly and can be quite useful in regions without a well-maintained radar network.

scholarly journals Spatio-temporal patterns of IED usage by the Provisional Irish Republican Army

2016 ◽  
Vol 27 (3) ◽  
pp. 377-402 ◽  
Author(s):  
STEPHEN TENCH ◽  
HANNAH FRY ◽  
PAUL GILL
Keyword(s):  
Process Model ◽  
Sociological Theory ◽  
Hawkes Process ◽  
Irish Republican Army ◽  
Improvised Explosive Device ◽  
The Troubles ◽  
Novel Approach ◽  
Explosive Device ◽  
Spatio Temporal ◽  
Improvised Explosive

In this paper, a unique dataset of improvised explosive device attacks during “The Troubles” in Northern Ireland (NI) is analysed via a Hawkes process model. It is found that this past dependent model is a good fit to improvised explosive device attacks yielding key insights about the nature of terrorism in NI. We also present a novel approach to quantitatively investigate some of the sociological theory surrounding the Provisional Irish Republican Army which challenges previously held assumptions concerning changes seen in the organisation. Finally, we extend our use of the Hawkes process model by considering a multidimensional version which permits both self and mutual-excitations. This allows us to test how the Provisional Irish Republican Army responded to past improvised explosive device attacks on different geographical scales from which we find evidence for the autonomy of the organisation over the six counties of NI and Belfast. By incorporating a second dataset concerning British Security Force (BSF) interventions, the multidimensional model allows us to test counter-terrorism (CT) operations in NI where we find subsequent increases in violence.

scholarly journals Spatio-temporal multidimensional collective data analysis for providing comfortable living anytime and anywhere

2018 ◽  
Vol 7 ◽  
Author(s):  
Naonori Ueda ◽  
Futoshi Naya
Keyword(s):  
Data Analysis ◽  
Real Time ◽  
Urban Areas ◽  
Temporal Data ◽  
Collective Data ◽  
Novel Approach ◽  
Smart Data ◽  
Spatio Temporal ◽  
World Information ◽  
Temporal Data Analysis

Machine learning is a promising technology for analyzing diverse types of big data. The Internet of Things era will feature the collection of real-world information linked to time and space (location) from all sorts of sensors. In this paper, we discuss spatio-temporal multidimensional collective data analysis to create innovative services from such spatio-temporal data and describe the core technologies for the analysis. We describe core technologies about smart data collection and spatio-temporal data analysis and prediction as well as a novel approach for real-time, proactive navigation in crowded environments such as event spaces and urban areas. Our challenge is to develop a real-time navigation system that enables movements of entire groups to be efficiently guided without causing congestion by making near-future predictions of people flow. We show the effectiveness of our navigation approach by computer simulation using artificial people-flow data.

scholarly journals A Spatial-Temporal Feature Extraction and Unsupervised Recognition of Video Anomalies in Real Time

2021 ◽  
pp. 66-72
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Prediction Models ◽  
Temporal Dynamics ◽  
Ground Truth ◽  
Reconstruction Error ◽  
Method Performance ◽  
Novel Approach ◽  
Spatio Temporal ◽  
Time Accuracy

Anomaly detection is an area of video analysis has a great importance in automated surveillance. Although it has been extensively studied, there has been little work started using CNN networks. Hence, in this thesis we presented a novel approach for learning motion features and modeling normal Spatio-temporal dynamics for anomaly detection. In our technique, we capture variations in scale of the patterns of motion in an image object by using optical flow dense estimation technique and train our auto encoder model using convolution long short term memories (ConvLSTM2D) as we are processing video frames and we predict the anomaly in real time using Euclidean distance between the generated and the ground truth frame and we achieved a real time accuracy of nearly 98% for the youtube videos which are not used for either testing or training. Error between the network’s output and the target output is used to classify a video volume as normal or abnormal. In addition to the use of reconstruction error, we also use prediction error for anomaly detection. The prediction models show comparable performance with state of the art methods. In comparison with the proposed method, performance is improved in one dataset. Moreover, running time is significantly faster.

scholarly journals Single Cortical Neurons as Deep Artificial Neural Networks

2019 ◽  
Author(s):  
David Beniaguev ◽  
Idan Segev ◽  
Michael London
Keyword(s):  
Neural Networks ◽  
Cortical Neurons ◽  
Deep Neural Networks ◽  
Pyramidal Neurons ◽  
Classical Architecture ◽  
Novel Approach ◽  
Cortical Pyramidal Neurons ◽  
Spatio Temporal ◽  
Cortical Pyramidal Cell

AbstractWe introduce a novel approach to study neurons as sophisticated I/O information processing units by utilizing recent advances in the field of machine learning. We trained deep neural networks (DNNs) to mimic the I/O behavior of a detailed nonlinear model of a layer 5 cortical pyramidal cell, receiving rich spatio-temporal patterns of input synapse activations. A Temporally Convolutional DNN (TCN) with seven layers was required to accurately, and very efficiently, capture the I/O of this neuron at the millisecond resolution. This complexity primarily arises from local NMDA-based nonlinear dendritic conductances. The weight matrices of the DNN provide new insights into the I/O function of cortical pyramidal neurons, and the approach presented can provide a systematic characterization of the functional complexity of different neuron types. Our results demonstrate that cortical neurons can be conceptualized as multi-layered “deep” processing units, implying that the cortical networks they form have a non-classical architecture and are potentially more computationally powerful than previously assumed.

scholarly journals A biological indicator for measuring and communicating local sea level rise captured in historic photographs.

2021 ◽  
Author(s):  
Philippe Max Rouja ◽  
Craig W Schneider ◽  
Vid Petrovic ◽  
Steve Blasco ◽  
Eric Lo ◽  
...  
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Biological Indicator ◽  
Rocky Intertidal ◽  
Intergovernmental Panel ◽  
Blue Green Algae ◽  
Digital Twin ◽  
Upward Shift ◽  
Novel Approach ◽  
Spatio Temporal

Abstract This paper explores a novel approach to collecting and communicating local site-specific data on recent sea level rise (SLR) using black zone biotic levels left on historic coastal stone structures by a stable community of cyanobacteria (blue-green algae) at the Royal Naval Dockyard in Bermuda. Photographs taken at the Dockyard in 1870, 2007 and 2017 show an upward shift in this living cyanobacterial community. A spatio-temporal digital twin computed from historic and contemporary photo assets was created to test the viability of these black zone lines as a proxy for sea level rise (SLR) measurements in Bermuda. Shifts in these black zone lines when analyzed through the digital twin demonstrate an average upward shift of 2.2 mm per year between 1870 and 2007 and 2.7 mm per year between 1870 and 2017, somewhat lower than the Global estimates from the Intergovernmental Panel on Climate Change Assessment Report predictions. However, the digital twin showed a dramatic upward shift of 8.8 cm between 2007 and 2017, or 8.8 mm per year, which coincided with Bermuda's highest recorded tidal extent since 1932. Black zone cyanobacteria are highly SLR sensitive and over long time scales comparative imagery of black zones could present a proper indicator of average sea level rise. At timescales less than 10 years the black zone may be best indicative of episodic tidal extent. As SLR will continue to shift supralittoral cyanobacteria upwards in Bermuda and in warm rocky intertidal zones worldwide, tidal monitoring and black zone assessments may prove to be a useful combination in documenting and communicating the reality, extent and possible acceleration of local SLR.

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