Temporal Gaussian Mixture Research Articles

Multi-Label Multi-Class Action Recognition With Deep Spatio-Temporal Layers Based on Temporal Gaussian Mixtures

Current action recognition studies enjoy the benefits of two neural network branches, spatial and temporal. This work aims to extend the previous work by introducing a fusion of spatial and temporal branches to provide superior action recognition capability toward multi-label multi-class classification problems. In this paper, we propose three fusion models with different fusion strategies. We first build several efficient temporal Gaussian mixture (TGM) layers to form spatial and temporal branches to learn a set of features. In addition to these branches, we introduce a new deep spatio-temporal branch consisting of a series of TGM layers to learn the features that emerged from the existing branches. Each branch produces a temporal-aware feature that assists the model in understanding the underlying action in a video. To verify the performance of our proposed models, we performed extensive experiments using the well-known MultiTHUMOS benchmarking dataset. The results demonstrate the importance of our proposed deep fusion mechanism, contributing to the overall score while keeping the number of parameters small.

Vision based smoke detection system using image energy and color information

Smoke detection is a crucial task in many video surveillance applications and could have a great impact to raise the level of safety of urban areas. Many commercial smoke detection sensors exist but most of them cannot be applied in open space or outdoor scenarios. With this aim, the paper presents a smoke detection system that uses a common CCD camera sensor to detect smoke in images and trigger alarms. First, a proper background model is proposed to reliably extract smoke regions and avoid over-segmentation and false positives in outdoor scenarios where many distractors are present, such as moving trees or light reflexes. A novel Bayesian approach is adopted to detect smoke regions in the scene analyzing image energy by means of the Wavelet Transform coefficients and Color Information. A statistical model of image energy is built, using a temporal Gaussian Mixture, to analyze the energy decay that typically occurs when smoke covers the scene then the detection is strengthen evaluating the color blending between a reference smoke color and the input frame. The proposed system is capable of detecting rapidly smoke events both in night and in day conditions with a reduced number of false alarms hence is particularly suitable for monitoring large outdoor scenarios where common sensors would fail. An extensive experimental campaign both on recorded videos and live cameras evaluates the efficacy and efficiency of the system in many real world scenarios, such as outdoor storages and forests.