Relative Dense Tracklets for Human Action Recognition

International audienceThis paper addresses the problem of recognizing human actions in video sequences for home care applications. Recent studies have shown that approaches which use a bag-of-words representation reach high action recognition accuracy. Unfortunately, these approaches have problems to discriminate similar actions, ignoring spatial information of features. As we focus on recognizing subtle differences in behaviour of patients, we propose a novel method which significantly enhances the discriminative properties of the bag-of-words technique. Our approach is based on a dynamic coordinate system, which introduces spatial information to the bag-of-words model, by computing relative tracklets. We perform an extensive evaluation of our approach on three datasets: popular KTH dataset, challenging ADL dataset and our collected Hospital dataset. Experiments show that our representation enhances the discriminative power of features and bag-of-words model, bringing significant improvements in action recognition performance

Automatic learning of gait signatures for people identification

This work targets people identification in video based on the way they walk (i.e. gait). While classical methods typically derive gait signatures from sequences of binary silhouettes, in this work we explore the use of convolutional neural networks (CNN) for learning high-level descriptors from low-level motion features (i.e. optical flow components). We carry out a thorough experimental evaluation of the proposed CNN architecture on the challenging TUM-GAID dataset. The experimental results indicate that using spatio-temporal cuboids of optical flow as input data for CNN allows to obtain state-of-the-art results on the gait task with an image resolution eight times lower than the previously reported results (i.e. 80x60 pixels).Comment: Proof of concept paper. Technical report on the use of ConvNets (CNN) for gait recognition. Data and code: http://www.uco.es/~in1majim/research/cnngaitof.htm

Robust abandoned object detection integrating wide area visual surveillance and social context

This paper presents a video surveillance framework that robustly and efficiently detects abandoned objects in surveillance scenes. The framework is based on a novel threat assessment algorithm which combines the concept of ownership with automatic understanding of social relations in order to infer abandonment of objects. Implementation is achieved through development of a logic-based inference engine based on Prolog. Threat detection performance is conducted by testing against a range of datasets describing realistic situations and demonstrates a reduction in the number of false alarms generated. The proposed system represents the approach employed in the EU SUBITO project (Surveillance of Unattended Baggage and the Identification and Tracking of the Owner)

Understanding Complex Human Behaviour in Images and Videos.

Understanding human motions and activities in images and videos is an important problem in many application domains, including surveillance, robotics, video indexing, and sports analysis. Although much progress has been made in classifying single person's activities in simple videos, little efforts have been made toward the interpretation of behaviors of multiple people in natural videos. In this thesis, I will present my research endeavor toward the understanding of behaviors of multiple people in natural images and videos. I identify four major challenges in this problem: i) identifying individual properties of people in videos, ii) modeling and recognizing the behavior of multiple people, iii) understanding human activities in multiple levels of resolutions and iv) learning characteristic patterns of interactions between people or people and surrounding environment. I discuss how we solve these challenging problems using various computer vision and machine learning technologies. I conclude with final remarks, observations, and possible future research directions.PhDElectrical Engineering: SystemsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/99956/1/wgchoi_1.pd

Novel Architecture for Human Re-Identification with a Two-Stream Neural Network and Attention Mechanism

This paper proposes a novel architecture that utilises an attention mechanism in conjunction with multi-stream convolutional neural networks (CNN) to obtain high accuracy in human re-identification (Reid). The proposed architecture consists of four blocks. First, the pre-processing block prepares the input data and feeds it into a spatial-temporal two-stream CNN (STC) with two fusion points that extract the spatial-temporal features. Next, the spatial-temporal attentional LSTM block (STA) automatically fine-tunes the extracted features and assigns weight to the more critical frames in the video sequence by using an attention mechanism. Extensive experiments on four of the most popular datasets support our architecture. Finally, the results are compared with the state of the art, which shows the superiority of this approach

Novel architecture for human re-identification with a two-stream neural network and attention ,echanism

This paper proposes a novel architecture that utilises an attention mechanism in conjunction with multi-stream convolutional neural networks (CNN) to obtain high accuracy in human re-identification (Reid). The proposed architecture consists of four blocks. First, the pre-processing block prepares the input data and feeds it into a spatial-temporal two-stream CNN (STC) with two fusion points that extract the spatial-temporal features. Next, the spatial-temporal attentional LSTM block (STA) automatically fine-tunes the extracted features and assigns weight to the more critical frames in the video sequence by using an attention mechanism. Extensive experiments on four of the most popular datasets support our architecture. Finally, the results are compared with the state of the art, which shows the superiority of this approach

Differential Recurrent Neural Networks for Human Activity Recognition

Human activity recognition has been an active research area in recent years. The difficulty of this problem lies in the complex dynamical motion patterns embedded through the sequential frames. The Long Short-Term Memory (LSTM) recurrent neural network is capable of processing complex sequential information since it utilizes special gating schemes for learning representations from long input sequences. It has the potential to model various time-series data, where the current hidden state has to be considered in the context of the past hidden states. Unfortunately, the conventional LSTMs do not consider the impact of spatio-temporal dynamics corresponding to the given salient motion patterns, when they gate the information that ought to be memorized through time. To address this problem, we propose a differential gating scheme for the LSTM neural network, which emphasizes the change in information gain caused by the salient motions between the successive video frames. This change in information gain is quantified by Derivative of States (DoS), and thus the proposed LSTM model is termed differential Recurrent Neural Network (dRNN). Based on the energy profiling of DoS, we further propose to employ the State Energy Profile (SEP) to search for salient dRNN states and construct more informative representations. To better understand the scene and human appearance information, the dRNN model is extended by connecting Convolutional Neural Networks (CNN) and stacked dRNNs into an end-to-end model. Lastly, the dissertation continues to discuss and compare the combined and the individual orders of DoS used within the dRNN. We propose to control the LSTM gates via individual order of DoS and stack multiple levels of LSTM cells in increasing orders of state derivatives. To this end, we have introduced a new family of LSTMs, expanding the applications of LSTMs and advancing the performances of the state-of-the-art methods

Contextual Statistics of Space-Time Ordered Features for Human Action Recognition

International audienceThe bag-of-words approach with local spatio-temporal features have become a popular video representation for action recognition. Recent methods have typically focused on capturing global and local statistics of features. However, existing approaches ignore relations between the features, particularly space-time arrangement of features, and thus may not be discriminative enough. Therefore, we propose a novel figure-centric representation which captures both local density of features and statistics of space-time ordered features. Using two benchmark datasets for human action recognition, we demonstrate that our representation enhances the discriminative power of features and improves action recognition performance, achieving 96.16% recognition rate on popular KTH action dataset and 93.33% on challenging ADL dataset