Background subtraction is the primary task of the majority of video inspection systems. The most important part of the background subtraction which is common among different algorithms is background modeling. In this regard, our paper addresses the problem of background modeling in a computationally efficient way, which is important for current eruption of "big data" processing coming from high resolution multi-channel videos. Our model is based on the assumption that background in natural images lies on a low-dimensional subspace. We formulated and solved this problem in a low-rank matrix completion framework. In modeling the background, we benefited from the in-face extended Frank-Wolfe algorithm for solving a defined convex optimization problem. We evaluated our fast robust matrix completion (fRMC) method on both background models challenge (BMC) and Stuttgart artificial background subtraction (SABS) datasets. The results were compared with the robust principle component analysis (RPCA) and low-rank robust matrix completion (RMC) methods, both solved by inexact augmented Lagrangian multiplier (IALM). The results showed faster computation, at least twice as when IALM solver is used, while having a comparable accuracy even better in some challenges, in subtracting the backgrounds in order to detect moving objects in the scene.

We present a novel approach for unsupervised detection of moving objects with non-salient movements (e.g. rodents in their home-cage). The proposed approach starts with separating the moving object from its background by modeling the background in a computationally efficient way. The background modeling is based on the assumption that background in natural videos lies on a low-dimensional subspace. We formulated and solved this problem using a low-rank matrix completion framework. To achieve computational efficiency, we proposed the fast robust matrix completion (fRMC) algorithm, which benefits from the in-face extended Frank-Wolfe approach as its optimization solver. We then augmented our fRMC-based moving object detection by incorporating the spatial information of the object as its objectness into the detection algorithm. With this augmentation we tackle the problem of non-salient motion. The proposed fRMC algorithm is evaluated on background models challenge (BMC2012) and Stuttgart artificial background subtraction (SABS) datasets. Its detection results are then compared with the popular methods of background subtraction based on robust principle component analysis (RPCA) and low-rank robust matrix completion (RMC) methods, solved by inexact augmented Lagrangian multiplier (IALM) and fast principal component pursuit via alternating minimization (FPCP). The outcomes showed faster computation, at least twice as when other methods are applied, while having a comparable detection accuracy. Moreover, fRMC observed to outperform the FPCP algorithm in background/foreground separation with minor computational overhead. Beyond that, we verified the performance improvement of the augmented fRMC with objectness on detecting the non-salient motion of in-cage mice using the Caltech resident-intruder mice (CRIM13) dataset. The evaluation showed 10% improvement in the detection performance, while significantly dropping the computational time.

All modern emotion theoretical views assume a role for peripheral physiological changes during emotional experiences. In this paper, we explored the correlation between autonomically-mediated changes in multimodal bodily signals and discrete emotional states. In order to fully exploit the information in each modality, week learners based on individual signal modalities are built and then fused to formed a robust inference model. To validate our model, three specific physiological signals including Electromyogram (EMG), Blood Volume Pressure (BVP) and Galvanic Skin Response (GSR) recorded during eight emotional states were analyzed. Our approach showed 88.1% emotion recognition accuracy, which outperformed the conventional Support Vector Machine (SVM) classifier with 17% accuracy improvement. Furthermore, in order to avoid information redundancy and the resultant over-fitting, a feature reduction method is proposed based on a correlation analysis to optimize the number of features required for training and validating each weak learner. Despite the feature space dimensionality reduction from 27 to 18 features, our methodology preserved the recognition accuracy of about 85.0%.

Objective monitoring and assessment of human motor behavior can improve the diagnosis and management of several medical conditions. Over the past decade, significant advances have been made in the use of wearable technology for continuously monitoring human motor behavior in free-living conditions. However, wearable technology remains ill-suited for applications which require monitoring and interpretation of complex motor behaviors (e.g., involving interactions with the environment). Recent advances in computer vision and deep learning have opened up new possibilities for extracting information from video recordings. In this paper, we present a hierarchical vision-based behavior phenotyping method for classification of basic human actions in video recordings performed using a single RGB camera. Our method addresses challenges associated with tracking multiple human actors and classification of actions in videos recorded in changing environments with different fields of view. We implement a cascaded pose tracker that uses temporal relationships between detections for short-term tracking and appearance based tracklet fusion for long-term tracking. Furthermore, for action classification, we use pose evolution maps derived from the cascaded pose tracker as low-dimensional and interpretable representations of the movement sequences for training a convolutional neural network. The cascaded pose tracker achieves an average accuracy of 88% in tracking the target human actor in our video recordings, and overall system achieves average test accuracy of 84% for target-specific action classification in untrimmed video recordings.

Prairie voles are socially monogamous rodents that form social bonds similar to those seen in primates. Social behavior investigation in these species, that include studying their breathing regulation, can provide us with an invaluable psychological model to understand social and emotional functions in both animals and humans. There have been several studies associated with the respiratory pattern of these species in the state of fear-induced defense. However, non-invasive measurement methods employed so far suffer from the lack of a natural experiment environment for the rodents. In this paper, we present a remote depth-based system, which applies a modified autocorrelation algorithm to automatically extract respiration patterns in small rodents. We evaluated our estimation accuracy through a series of experiments and comparing the extracted results with breathing rates obtained from visual inspection of synchronously collected RGB videos. In a preliminary test on a human participant, breathing rate was estimated with 100% accuracy, while the estimation accuracy was 94.8% for a restrained vole. Finally, we monitored the respiratory alternations of three voles in transition from a baseline, to a fearful state, and back to a normal state; the estimated breathing rates confirmed the existing hypothesis regarding animal defense strategies.

Automatic tracking of rodents' behaviors over time in their home cages is of great interest in psycho-physiological studies. The commercially-available animal monitoring systems use RGB videos or bio-potential signals to monitor behaviors of animals when exploring their surroundings. The based models of these devices starts from several thousands of dollars and the cost would increase if extra analysis features were added. In this study, we present a low-cost, non-contact animal tracking system which records depth data from the caged rodent to detect the animal's location and pose over time. An adaptive Gaussian Mixture Model (GMM) algorithm is employed to detect animal's center of mass and extract its movement trajectory over an extended period of time. The animal's pose is determined by applying Principle Component Analysis (PCA) on 3D depth data of the located animal. In conjunction with our previously-introduced respiratory detection algorithm, this system can be utilized as an automatic long-term and unobtrusive monitoring system for animal experiments. We validated the tracking accuracy of our system by monitoring two different caged voles. The voles' locations were correctly detected in 80% of times, while the poses were detected correctly in 100% of times confirmed by visually inspecting the color-coded depth videos.