Improved partitioning technique for density cube-based spatio-temporal clustering method

This work proposes a novel partitioning technique on the density-cube-based data model for the Spatio-temporal clustering method. This work further adapts this clustering approach to Spatio-temporal data. We have compared the IMSTAGRID-the proposed algorithm to the ST-DBSCAN, AGRID+, and ST-AGRID algorithms and have found that the IMSTAGRID algorithm improves the data partitioning technique and the interval expansion technique and is able to achieve uniformity in the spatial and temporal dimensional values. Three types of Spatio-temporal data sets have been used in this experiment: a storm data set and two synthetic data sets – synthetic data set 1 and synthetic data set 2. Both the storm data set and synthetic data set 2 were comparable in terms of the scattering of the data points, while synthetic data set 1 contained clustered data. The performance of the IMSTAGRID clustering method was measured via a silhouette analysis, and its results surpassed the other algorithms investigated; the silhouette index for synthetic data set 2 was 0.970, and 0.993 using synthetic data set data set 1. The IMSTAGRID algorithm also outperformed the baseline algorithms (ST-DBSCAN, AGRID+, and ST-AGRID) in labeling accuracy for the storm data set, yielding results of 82.68%, 38.36%, 76.13%, and 78.66%, respectively

Towards Intelligent Crowd Behavior Understanding through the STFD Descriptor Exploration

Realizing the automated and online detection of crowd anomalies from surveillance CCTVs is a research-intensive and application-demanding task. This research proposes a novel technique for detecting crowd abnormalities through analyzing the spatial and temporal features of input video signals. This integrated solution defines an image descriptor (named spatio-temporal feature descriptor - STFD) that reflects the global motion information of crowds over time. A CNN has then been adopted to classify dominant or large-scale crowd abnormal behaviors. The work reported has focused on: 1) detecting moving objects in online (or near real-time) manner through spatio-temporal segmentations of crowds that is defined by the similarity of group trajectory structures in temporal space and the foreground blocks based on Gaussian Mixture Model (GMM) in spatial space; 2) dividing multiple clustered groups based on the spectral clustering method by considering image pixels from spatio-temporal segmentation regions as dynamic particles; 3) generating the STFD descriptor instances by calculating the attributes (i.e., collectiveness, stability, conflict and crowd density) of particles in the corresponding groups; 4) inputting generated STFD descriptor instances into the devised convolutional neural network (CNN) to detect suspicious crowd behaviors. The test and evaluation of the devised models and techniques have selected the PETS database as the primary experimental data sets. Results against benchmarking models and systems have shown promising advancements of this novel approach in terms of accuracy and efficiency for detecting crowd anomalies

Towards Intelligent Crowd Behavior Understanding through the STFD Descriptor Exploration

Realizing the automated and online detection of crowd anomalies from surveillance CCTVs is a research-intensive and application-demanding task. This research proposes a novel technique for detecting crowd abnormalities through analyzing the spatial and temporal features of input video signals. This integrated solution defines an image descriptor (named spatio-temporal feature descriptor - STFD) that reflects the global motion information of crowds over time. A CNN has then been adopted to classify dominant or large-scale crowd abnormal behaviors. The work reported has focused on: 1) detecting moving objects in online (or near real-time) manner through spatio-temporal segmentations of crowds that is defined by the similarity of group trajectory structures in temporal space and the foreground blocks based on Gaussian Mixture Model (GMM) in spatial space; 2) dividing multiple clustered groups based on the spectral clustering method by considering image pixels from spatio-temporal segmentation regions as dynamic particles; 3) generating the STFD descriptor instances by calculating the attributes (i.e., collectiveness, stability, conflict and crowd density) of particles in the corresponding groups; 4) inputting generated STFD descriptor instances into the devised convolutional neural network (CNN) to detect suspicious crowd behaviors. The test and evaluation of the devised models and techniques have selected the PETS database as the primary experimental data sets. Results against benchmarking models and systems have shown promising advancements of this novel approach in terms of accuracy and efficiency for detecting crowd anomalies