3D Human Activity Recognition with Reconfigurable Convolutional Neural Networks

Human activity understanding with 3D/depth sensors has received increasing attention in multimedia processing and interactions. This work targets on developing a novel deep model for automatic activity recognition from RGB-D videos. We represent each human activity as an ensemble of cubic-like video segments, and learn to discover the temporal structures for a category of activities, i.e. how the activities to be decomposed in terms of classification. Our model can be regarded as a structured deep architecture, as it extends the convolutional neural networks (CNNs) by incorporating structure alternatives. Specifically, we build the network consisting of 3D convolutions and max-pooling operators over the video segments, and introduce the latent variables in each convolutional layer manipulating the activation of neurons. Our model thus advances existing approaches in two aspects: (i) it acts directly on the raw inputs (grayscale-depth data) to conduct recognition instead of relying on hand-crafted features, and (ii) the model structure can be dynamically adjusted accounting for the temporal variations of human activities, i.e. the network configuration is allowed to be partially activated during inference. For model training, we propose an EM-type optimization method that iteratively (i) discovers the latent structure by determining the decomposed actions for each training example, and (ii) learns the network parameters by using the back-propagation algorithm. Our approach is validated in challenging scenarios, and outperforms state-of-the-art methods. A large human activity database of RGB-D videos is presented in addition.Comment: This manuscript has 10 pages with 9 figures, and a preliminary version was published in ACM MM'14 conferenc

Human gesture recognition under degraded environments using 3D-integral imaging and deep learning

In this paper, we propose a spatio-temporal human gesture recognition algorithm under degraded conditions using three-dimensional integral imaging and deep learning. The proposed algorithm leverages the advantages of integral imaging with deep learning to provide an efficient human gesture recognition system under degraded environments such as occlusion and low illumination conditions. The 3D data captured using integral imaging serves as the input to a convolutional neural network (CNN). The spatial features extracted by the convolutional and pooling layers of the neural network are fed into a bi-directional long short-term memory (BiLSTM) network. The BiLSTM network is designed to capture the temporal variation in the input data. We have compared the proposed approach with conventional 2D imaging and with the previously reported approaches using spatio-temporal interest points with support vector machines (STIP-SVMs) and distortion invariant non-linear correlation-based filters. Our experimental results suggest that the proposed approach is promising, especially in degraded environments. Using the proposed approach, we find a substantial improvement over previously published methods and find 3D integral imaging to provide superior performance over the conventional 2D imaging system. To the best of our knowledge, this is the first report that examines deep learning algorithms based on 3D integral imaging for human activity recognition in degraded environments

Temporal Relational Reasoning in Videos

Temporal relational reasoning, the ability to link meaningful transformations of objects or entities over time, is a fundamental property of intelligent species. In this paper, we introduce an effective and interpretable network module, the Temporal Relation Network (TRN), designed to learn and reason about temporal dependencies between video frames at multiple time scales. We evaluate TRN-equipped networks on activity recognition tasks using three recent video datasets - Something-Something, Jester, and Charades - which fundamentally depend on temporal relational reasoning. Our results demonstrate that the proposed TRN gives convolutional neural networks a remarkable capacity to discover temporal relations in videos. Through only sparsely sampled video frames, TRN-equipped networks can accurately predict human-object interactions in the Something-Something dataset and identify various human gestures on the Jester dataset with very competitive performance. TRN-equipped networks also outperform two-stream networks and 3D convolution networks in recognizing daily activities in the Charades dataset. Further analyses show that the models learn intuitive and interpretable visual common sense knowledge in videos.Comment: camera-ready version for ECCV'1

Radar Based Activity Recognition using CNN-LSTM Network Architecture

Human Activity Recognition based research has got intensified based on the evolving demand of smart systems. There has been already a lot of wearables, digital smart sensors deployed to classify various activities. Radar sensor-based Activity recognition has been an active research area during recent times. In order to classify the radar micro doppler signature images we have proposed a approach using Convolutional Neural Network-Long Short Term Memory (CNN-LSTM). Convolutional Layer is used to update the filter values to learn the features of the radar images. LSTM Layer enhances the temporal information besides the features obtained through Convolutional Neural Network. We have used a dataset published by University of Glasgow that captures six activities for 56 subjects under different ages, which is a first of its kind dataset unlike the signals captured under controlled lab environment. Our Model has achieved 96.8% for the training data and 93.5% for the testing data. The proposed work has outperformed the existing traditional deep learning Architectures

Industrial Human Activity Prediction and Detection Using Sequential Memory Networks

Prediction of human activity and detection of subsequent actions is crucial for improving the interaction between humans and robots during collaborative operations. Deep-learning techniques are being applied to recognize human activities, including industrial applications. However, the lack of sufficient dataset in the industrial domain and complexities of some industrial activities such as screw driving, assembling small parts, and others affect the model development and testing of human activities. The InHard dataset (Industrial Human Activity Recognition Dataset) was recently published to facilitate industrial human activity recognition for better human-robot collaboration, which still lacks extended evaluation. We propose an activity recognition method using a combined convolutional neural network (CNN) and long short-term memory (LSTM) techniques to evaluate the InHard dataset and compare it with a new dataset captured in a lab environment. This method improves the success rate of activity recognition by processing temporal and spatial information. Accordingly, the accuracy of the dataset is tested using labeled lists of activities from IMU and video data. A model is trained and tested for nine low-level activity classes with approximately 400 samples per class. The test result shows 88% accuracy for IMU-based skeleton data, 77% for RGB spatial video, and 63% for RGB video-based skeleton. The result has been verified using a previously published region-based activity recognition. The proposed approach can be extended to push the cognition capability of robots in human-centric workplaces

Multiscale human activity recognition and anticipation network

Deep convolutional neural networks have been leveraged to achieve huge improvements in video understanding and human activity recognition performance in the past decade. However, most existing methods focus on activities that have similar time scales, leaving the task of action recognition on multiscale human behaviors less explored. In this study, a two-stream multiscale human activity recognition and anticipation (MS-HARA) network is proposed, which is jointly optimized using a multitask learning method. The MS-HARA network fuses the two streams of the network using an efficient temporal-channel attention (TCA)-based fusion approach to improve the model's representational ability for both temporal and spatial features. We investigate the multiscale human activities from two basic categories, namely, midterm activities and long-term activities. The network is designed to function as part of a real-time processing framework to support interaction and mutual understanding between humans and intelligent machines. It achieves state-of-the-art results on several datasets for different tasks and different application domains. The midterm and long-term action recognition and anticipation performance, as well as the network fusion, are extensively tested to show the efficiency of the proposed network. The results show that the MS-HARA network can easily be extended to different application domains

Human Action Recognition in Videos using Convolution Long Short-Term Memory Network with Spatio-Temporal Networks

Two-stream convolutional networks plays an essential role as a powerful feature extractor in human action recognition in videos. Recent studies have shown the importance of two-stream Convolutional Neural Networks (CNN) to recognize human action recognition. Recurrent Neural Networks (RNN) has achieved the best performance in video activity recognition combining CNN. Encouraged by CNN's results with RNN, we present a two-stream network with two CNNs and Convolution Long-Short Term Memory (CLSTM). First, we extricate Spatio-temporal features using two CNNs using pre-trained ImageNet models. Second, the results of two CNNs from step one are combined and fed as input to the CLSTM to get the overall classification score. We also explored the various fusion function performance that combines two CNNs and the effects of feature mapping at different layers. And, conclude the best fusion function along with layer number. To avoid the problem of overfitting, we adopt the data augmentation techniques. Our proposed model demonstrates a substantial improvement compared to the current two-stream methods on the benchmark datasets with 70.4% on HMDB-51 and 95.4% on UCF-101 using the pre-trained ImageNet model. Doi: 10.28991/esj-2021-01254 Full Text: PD

Activity Recognition based on a Magnitude-Orientation Stream Network

The temporal component of videos provides an important clue for activity recognition, as a number of activities can be reliably recognized based on the motion information. In view of that, this work proposes a novel temporal stream for two-stream convolutional networks based on images computed from the optical flow magnitude and orientation, named Magnitude-Orientation Stream (MOS), to learn the motion in a better and richer manner. Our method applies simple nonlinear transformations on the vertical and horizontal components of the optical flow to generate input images for the temporal stream. Experimental results, carried on two well-known datasets (HMDB51 and UCF101), demonstrate that using our proposed temporal stream as input to existing neural network architectures can improve their performance for activity recognition. Results demonstrate that our temporal stream provides complementary information able to improve the classical two-stream methods, indicating the suitability of our approach to be used as a temporal video representation.Comment: 8 pages, SIBGRAPI 201