Crowd Behavior Understanding through SIOF Feature Analysis

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 the input video signals. This integrated solution defines an image descriptor that reflects the global motion information over time. A non-linear SVM has then been adopted to classify dominant or large-scale crow d abnormal behaviors. The work reported has focused on: 1) online (or near real-time) detection of moving objects through a background subtraction model, namely ViBe; and to identify the saliency information as a spatial feature in addition to the optical flow of the motion foreground as the temporal feature; 2) to combine the extracted spatial and temporal features into a novel SIOF descriptor that encapsulates the global movement characteristic of a crowd; 3) the optimization of a nonlinear support vector machine (SVM) as classifier to detect suspicious crowd behaviors. The test and evaluation of the devised models and techniques have selected the BEHAVE database as the primary experimental data sets. Results against benchmarking models and systems have shown promising advancements in terms of the accuracy and efficiency for detecting crowd anomalies

Audio-visual multi-modality driven hybrid feature learning model for crowd analysis and classification

The high pace emergence in advanced software systems, low-cost hardware and decentralized cloud computing technologies have broadened the horizon for vision-based surveillance, monitoring and control. However, complex and inferior feature learning over visual artefacts or video streams, especially under extreme conditions confine majority of the at-hand vision-based crowd analysis and classification systems. Retrieving event-sensitive or crowd-type sensitive spatio-temporal features for the different crowd types under extreme conditions is a highly complex task. Consequently, it results in lower accuracy and hence low reliability that confines existing methods for real-time crowd analysis. Despite numerous efforts in vision-based approaches, the lack of acoustic cues often creates ambiguity in crowd classification. On the other hand, the strategic amalgamation of audio-visual features can enable accurate and reliable crowd analysis and classification. Considering it as motivation, in this research a novel audio-visual multi-modality driven hybrid feature learning model is developed for crowd analysis and classification. In this work, a hybrid feature extraction model was applied to extract deep spatio-temporal features by using Gray-Level Co-occurrence Metrics (GLCM) and AlexNet transferrable learning model. Once extracting the different GLCM features and AlexNet deep features, horizontal concatenation was done to fuse the different feature sets. Similarly, for acoustic feature extraction, the audio samples (from the input video) were processed for static (fixed size) sampling, pre-emphasis, block framing and Hann windowing, followed by acoustic feature extraction like GTCC, GTCC-Delta, GTCC-Delta-Delta, MFCC, Spectral Entropy, Spectral Flux, Spectral Slope and Harmonics to Noise Ratio (HNR). Finally, the extracted audio-visual features were fused to yield a composite multi-modal feature set, which is processed for classification using the random forest ensemble classifier. The multi-class classification yields a crowd-classification accurac12529y of (98.26%), precision (98.89%), sensitivity (94.82%), specificity (95.57%), and F-Measure of 98.84%. The robustness of the proposed multi-modality-based crowd analysis model confirms its suitability towards real-world crowd detection and classification tasks

Spartan Daily, January 10, 1964

Volume 51, Issue 64https://scholarworks.sjsu.edu/spartandaily/4564/thumbnail.jp

Alumni Magazine December 1985

https://digitalcommons.whitworth.edu/alumnimagazine/1365/thumbnail.jp

The BG News October 9, 1987

The BGSU campus student newspaper October 9, 1987. Volume 70 - Issue 28https://scholarworks.bgsu.edu/bg-news/5703/thumbnail.jp

Casco Bay Weekly : 5 November 1998

https://digitalcommons.portlandlibrary.com/cbw_1998/1046/thumbnail.jp

The BG News August 25, 2003

The BGSU campus student newspaper August 25, 2003. Volume 94 - Issue 2https://scholarworks.bgsu.edu/bg-news/8139/thumbnail.jp

Great Republic: a historical and archaeological analysis of a Pacific mail steamship

In 1986 the remains of a shipwreck were discovered on Sand Island in the mouth of the Columbia River in the Pacific Northwest. The following year, a team of archaeologists investigated the site in order to determine its original identity. After a series of dives, the team concluded that the wreck was the remains of the Hudson’s Bay Company brig, Isabella, a ship that was lost in that area in 1830. Recent investigations on the shipwreck disproved this identity. The turbulent conditions of the Columbia River have helped researchers by shifting a significant amount of sand overburden away from the vessel, exposing a greater area of the ship. With this new information, the wreck is now believed to be the remains of the wooden side-wheel steamer Great Republic that belonged to the Pacific Mail Steamship Company, rather than Isabella. This thesis investigates the history of Great Republic and its role in American maritime history, as well as its possible archaeological remains at the bottom of the Columbia River. In order to provide a clear and concise story, I begin with the history of the Pacific Mail Steamship Company and its importance in the development of the western coast of the United States. Since Great Republic was integral to the Asian trade of the nineteenth century, the second portion of the thesis is dedicated to Asian-American commercial and political relations during the nineteenth century. Great Republic and its three sister ships are then described and analyzed in detail based on contemporary sources. Finally, the archaeological evidence is assessed beginning with the discovery of the wreck. I detail the investigations and discoveries made on the wreck over the last 20 years. In my conclusions I discuss the importance of Great Republic from a historical standpoint and emphasize its place in American maritime history. I also detail key aspects concerning the wreck that I believe are imperative for future research. Though the remains convincingly appear to be those of Great Republic there are still structural features that need to be analyzed before a positive identification is possible