Recognize Human Activities from Partially Observed Videos

Human action recognition (HAR) is a challenging problem because of the complexity and similarity in different actions. In recent years, many methods have been proposed for HAR. Sparse coding-based approaches have been widely used in this field. Also, many works have been done based on manifold learning theory. When the videos are similar but from different classes, their sparse codes may be similar and the actions might be classified mistakenly. In this paper, a multi-modal affine graph regularized sparse coding approach is proposed for solving this problem in HAR. At first, HOG3D, HOG/Hof and SURF3D descriptors were extracted from the action datasets, then the sparse codes have been obtained for each descriptor using the proposed method. The dictionary learning method used in this step has more discrimination power in respect to the traditional methods. Then, these codes are scored differently using SVM classifier and at last a Naïve Bayes leads to a final decision. Experiments on KTH, Weizmann and UCF Sport action datasets show that the proposed method can significantly outperform several previous methods in human action classification especially in real-world data.

Human activity recognition is concerned with identifying the specific movement of a person based on sensor data. In recent years, many different techniques have been proposed for modelling and recognising human activities, with a specific focus on the development of approaches to classifying human activities using deep learning techniques. The research presented in this paper proposes a fuzzy feature representation approach to represent occupancy sensor data, along with a Convolutional Neural Network Classifier (CNNC) for human activity modelling and recognition. Sensory data gathered from a home environment are converted into occupancy data representing human activities and then fuzzified before being fed as inputs into the CNNC. The learning capability of CNNC allows the model to learn the relationship between the fuzzified inputs and their corresponding output activities during training mode. The relations learned in the trained CNNC model are then used to identify human activity patterns and classify these when the testing dataset is applied. The proposed method is evaluated using a dataset representing activities of daily living for a single user gathered from a real-home environment.

In this paper, we present a home-monitoring oriented human activity recognition benchmark database, based on the combination of a color video camera and a depth sensor. Our contributions are two-fold: 1) We have created a publicly releasable human activity video database (i.e., named as RGBD-HuDaAct), which contains synchronized color-depth video streams, for the task of human daily activity recognition. This database aims at encouraging more research efforts on human activity recognition based on multi-modality sensor combination (e.g., color plus depth). 2) Two multi-modality fusion schemes, which naturally combine color and depth information, have been developed from two state-of-the-art feature representation methods for action recognition, i.e., spatio-temporal interest points (STIPs) and motion history images (MHIs). These depth-extended feature representation methods are evaluated comprehensively and superior recognition performances over their uni-modality (e.g., color only) counterparts are demonstrated.

Encoding human activities using multimodal wearable sensory data

Real-time human activity recognition is essential for human-robot interactions for assisted healthy independent living. Most previous work in this area is performed on traditional two-dimensional (2D) videos and both global and local methods have been used. Since 2D videos are sensitive to changes of lighting condition, view angle, and scale, researchers begun to explore applications of 3D information in human activity understanding in recently years. Unfortunately, features that work well on 2D videos usually don't perform well on 3D videos and there is no consensus on what 3D features should be used. Here we propose a model of human activity recognition based on 3D movements of body joints. Our method has three steps, learning dictionaries of sparse codes of 3D movements of joints, sparse coding, and classification. In the first step, space-time volumes of 3D movements of body joints are obtained via dense sampling and independent component analysis is then performed to construct a dictionary of sparse codes for each activity. In the second step, the space-time volumes are projected to the dictionaries and a set of sparse histograms of the projection coefficients are constructed as feature representations of the activities. Finally, the sparse histograms are used as inputs to a support vector machine to recognize human activities. We tested this model on three databases of human activities and found that it outperforms the state-of-the-art algorithms. Thus, this model can be used for real-time human activity recognition in many applications.

Neuromorphic computing with hybrid CNN–Stochastic Reservoir for time series WiFi based human activity recognition

Human activity modeling and recognition using wearable sensors is important in pervasive healthcare, with applications including quantitative assessment of motor function, rehabilitation, and elder care. Previous human activity recognition techniques use a whole-motion model in which continuous sensor streams are divided into windows with a fixed time duration whose length is chosen such that all the relevant information in each activity signal can be extracted from each window. In this paper, we present a statistical motion primitive-based framework for human activity representation and recognition. Our framework is based on Bag-of-Features (BoF), which builds activity models using histograms of primitive symbols. We experimentally validate the effectiveness the BoF-based framework for recognizing nine activity classes and evaluate six factors which impact the performance of the framework. The factors include window size, choices of features, methods to construct motion primitives, motion vocabulary size, weighting schemes of motion primitive assignments, and learning machine kernel functions. Finally, we demonstrate that our statistical BoF-based framework can achieve much better performance compared to a non-statistical string-matching-based approach.

An automatic human action representation and recognition technique is proposed in this paper. Appearance-change problem due to human wearing dresses and body shapes is also investigated in this study for automatic human action recognition. A tuned eigenspace technique is proposed for automatic human posture and/or motion recognition that successfully overcome the preceding problems. We employ image pre-processing by Gaussian and Sobel edge filter, called the first stage tuning, for reducing a dress effect, and a mean eigenspace produced by taking a mean of the similar postures, called the second stage tuning, for avoiding the preceding problems. An eigenspace called a tuned eigenspace is obtained from the mentioned processes and it is used for further recognition of unfamiliar postures and actions. The proposed method is compared with a related technique and the robustness of this approach is presented.

Gait recognition systems often suffer from the challenges when query gaits are under the coupled effects of unknown view angles and large intra-class variations (e.g., wearing a coat). In this paper, we deem it as a two-stage classification problem, namely, view detection and fixed-view gait recognition. First, we propose two simple yet effective feature types (i.e., global features and local features) for view detection. By using the detected view information, the corresponding gallery (i.e., enrolled gait) for the detected view can be adaptively selected to perform the fixed-view gait recognition. For fixed-view gait recognition, since the inter-class variations for training are normally small, whereas the query gait usually has large intra-class variations, random subspace method are adopted. We evaluate our approach on the largest multi-view gait database CASIA-B dataset. The avoidance of searching whole multi-view database as well as the competitive performance indicate that our proposed method is practical for gait recognition in real world surveillance scenarios.

Enhancement of human 3D pose estimation using a novel concept of depth prediction with pose alignment from a single 2D image

Smartphones are increasing ubiquitously due to the need and demand in the modern era. The world is transmuting into a global village with the cumulation of smart devices. Nowadays, smartphones are enriched with inertial sensors which can be used to recognize physical human activities in the wild. Human activity recognition (HAR) lies at the core of many applications like health monitoring, fall detection, road safety, personal assistance, and behavior-based context awareness. Context-based HAR is a new dimension that provides fine-grained information about the action being performed and leads us towards the automated and intelligent system design, which is useful to furnish the smart solution to real-life problems. In this paper, smartphone sensors are utilized to propose a framework for human activity and context recognition. This framework performs activity-dependent context recognition on the ExtraSensory dataset using random forest, decision tree, and k-nearest neighbor classifiers, and achieves an accuracy of 93.10%, demonstrating the effectiveness of the proposed framework.

With the rapid development of the computer and sensor field, inertial sensor data have been widely used in human activity recognition. At present, most relevant studies divide human activities into basic actions and transitional actions, in which basic actions are classified by unified features, while transitional actions usually use context information to determine the category. For the existing single method that cannot well realize human activity recognition, this paper proposes a human activity classification and recognition model based on smartphone inertial sensor data. The model fully considers the feature differences of different properties of actions, uses a fixed sliding window to segment the human activity data of inertial sensors with different attributes and, finally, extracts the features and recognizes them on different classifiers. The experimental results show that dynamic and transitional actions could obtain the best recognition performance on support vector machines, while static actions could obtain better classification effects on ensemble classifiers; as for feature selection, the frequency-domain feature used in dynamic action had a high recognition rate, up to 99.35%. When time-domain features were used for static and transitional actions, higher recognition rates were obtained, 98.40% and 91.98%, respectively.

Human motion detection and activity recognition are becoming vital for the applications in smart homes. Traditional Human Activity Recognition (HAR) mechanisms use special devices to track human motions, such as cameras (vision-based) and various types of sensors (sensor-based). These mechanisms are applied in different applications, such as home security, Human–Computer Interaction (HCI), gaming, and healthcare. However, traditional HAR methods require heavy installation, and can only work under strict conditions. Recently, wireless signals have been utilized to track human motion and HAR in indoor environments. The motion of an object in the test environment causes fluctuations and changes in the Wi-Fi signal reflections at the receiver, which result in variations in received signals. These fluctuations can be used to track object (i.e., a human) motion in indoor environments. This phenomenon can be improved and leveraged in the future to improve the internet of things (IoT) and smart home devices. The main Wi-Fi sensing methods can be broadly categorized as Received Signal Strength Indicator (RSSI), Wi-Fi radar (by using Software Defined Radio (SDR)) and Channel State Information (CSI). CSI and RSSI can be considered as device-free mechanisms because they do not require cumbersome installation, whereas the Wi-Fi radar mechanism requires special devices (i.e., Universal Software Radio Peripheral (USRP)). Recent studies demonstrate that CSI outperforms RSSI in sensing accuracy due to its stability and rich information. This paper presents a comprehensive survey of recent advances in the CSI-based sensing mechanism and illustrates the drawbacks, discusses challenges, and presents some suggestions for the future of device-free sensing technology.

In this era, deep learning methods offer a broad spectrum of efficient and original algorithms to recognize or predict an output when given a sequence of inputs. In current trends, deep learning methods using recent long short-term memory (LSTM) algorithms try to provide superior performance, but they still have limited effectiveness when detecting sequences of complex human activity. In this work, we adapted the LSTM algorithm into a synchronous algorithm (sync-LSTM), enabling the model to take multiple parallel input sequences to produce multiple parallel synchronized output sequences. The proposed method is implemented for simultaneous human activity recognition (HAR) using heterogeneous sensor data in a smart home. HAR assists artificial intelligence in providing services to users according to their preferences. The sync-LSTM algorithm improves learning performance and sees its potential for real-world applications in complex HAR, such as concurrent activity, with higher accuracy and satisfactory computational complexity. The adapted algorithm for HAR is also applicable in the fields of ambient assistive living, healthcare, robotics, pervasive computing, and astronomy. Extensive experimental evaluation with publicly available datasets demonstrates the competitive recognition capabilities of our approach. The sync-LSTM algorithm improves learning performance and has the potential for real-life applications in complex HAR. For concurrent activity recognition, our proposed method shows an accuracy of more than 97%.

In recent years, it has been observed that many researchers have been working on different areas of detection, recognition and monitoring of human activities. The automatic determination of human physical activities is often referred to as human activity recognition (HAR). One of the most important technology that detects and tracks the activity of the human body is sensor-based HAR technology. In recent days, sensor-based HAR attracts attention in the field of computers due to its wide use in daily life and is a rapidly growing field of research. Activity recognition (AR) application is carried out by evaluating the signals obtained from various sensors placed in the human body. In this study, a new approach is proposed to extract features from sensor signals using HAR. The proposed approach is inspired by the Gray Level Co-Occurrence Matrix (GLCM) method, which is widely used in image processing, but it is applied to one-dimensional signals, unlike GLCM. Two datasets were used to test the proposed approach. The datasets were created from the signals obtained from the accelerometer, gyro and magnetometer sensors. Heralick features were obtained from co-occurrence matrix created after 1D-GLCM (One (1) Dimensional-Gray Level Co-Occurrence Matrix) was applied to the signals. HAR operation has been carried out for different scenarios using these features. Success rates of 96.66 and 93.88% were obtained for two datasets, respectively. It has been observed that the new approach proposed within the scope of the study provides high success rates for HAR applications. It is thought that the proposed approach can be used in the classification of different signals.