Audio Based Event Detection for Multimedia Surveillance

Abstract

With the increasing use of audio sensors in surveillance and monitoring applications, event detection using audio streams has emerged as an important research problem. This paper presents a hierarchical approach for audio based event detection for surveillance. The proposed approach first classifies a given audio frame into vocal and nonvocal events, and then performs further classification into normal and excited events. We model the events using a Gaussian Mixture Model and optimize the parameters for four different audio features ZCR, LPC, LPCC and LFCC. Experiments have been performed to evaluate the effectiveness of the features for detecting various normal and the excited state human activities. The results show that the proposed top-down event detection approach works significantly better than the single level approach.

LifeLogging: Personal Big Data

“…Automatic recognition of events, where the event has some semantic significance, has been identified as important for many years in applications like managing personal photos, as described in Lim et al (2003), indexing surveillance videos by Atrey et al (2006) and summarising sports videos in work by Sadlier and O'Connor (2005). In fact a whole series of workshops have been held with a specific focus on the automatic identification of events from data streams, Doulamis et al (2008); Scherp et al (2010).…”

On the Joint Use of NMF and Classification for Overlapping Acoustic Event Detection

“…With audio being a crucial modality in multimodal content, most common applications of AED include smart home environments, surveillance and security [1,2], as well as multimedia database retrieval.…”

Detection of anomalous events in a tennis game using multimodal information

“…Some work [8], [9], [10], has put more focus on audio information. [8] employed a spectral clustering algorithm to discover the audio elements.…”