Going Deeper into Action Recognition: A Survey

Understanding human actions in visual data is tied to advances in complementary research areas including object recognition, human dynamics, domain adaptation and semantic segmentation. Over the last decade, human action analysis evolved from earlier schemes that are often limited to controlled environments to nowadays advanced solutions that can learn from millions of videos and apply to almost all daily activities. Given the broad range of applications from video surveillance to human-computer interaction, scientific milestones in action recognition are achieved more rapidly, eventually leading to the demise of what used to be good in a short time. This motivated us to provide a comprehensive review of the notable steps taken towards recognizing human actions. To this end, we start our discussion with the pioneering methods that use handcrafted representations, and then, navigate into the realm of deep learning based approaches. We aim to remain objective throughout this survey, touching upon encouraging improvements as well as inevitable fallbacks, in the hope of raising fresh questions and motivating new research directions for the reader

Estimating Carotid Pulse and Breathing Rate from Near-infrared Video of the Neck

Objective: Non-contact physiological measurement is a growing research area that allows capturing vital signs such as heart rate (HR) and breathing rate (BR) comfortably and unobtrusively with remote devices. However, most of the approaches work only in bright environments in which subtle photoplethysmographic and ballistocardiographic signals can be easily analyzed and/or require expensive and custom hardware to perform the measurements. Approach: This work introduces a low-cost method to measure subtle motions associated with the carotid pulse and breathing movement from the neck using near-infrared (NIR) video imaging. A skin reflection model of the neck was established to provide a theoretical foundation for the method. In particular, the method relies on template matching for neck detection, Principal Component Analysis for feature extraction, and Hidden Markov Models for data smoothing. Main Results: We compared the estimated HR and BR measures with ones provided by an FDA-cleared device in a 12-participant laboratory study: the estimates achieved a mean absolute error of 0.36 beats per minute and 0.24 breaths per minute under both bright and dark lighting. Significance: This work advances the possibilities of non-contact physiological measurement in real-life conditions in which environmental illumination is limited and in which the face of the person is not readily available or needs to be protected. Due to the increasing availability of NIR imaging devices, the described methods are readily scalable.Comment: 21 pages, 15 figure

Periodic Motion Detection and Estimation via Space-Time Sampling

A novel technique to detect and localize periodic movements in video is presented. The distinctive feature of the technique is that it requires neither feature tracking nor object segmentation. Intensity patterns along linear sample paths in space-time are used in estimation of period of object motion in a given sequence of frames. Sample paths are obtained by connecting (in space-time) sample points from regions of high motion magnitude in the first and last frames. Oscillations in intensity values are induced at time instants when an object intersects the sample path. The locations of peaks in intensity are determined by parameters of both cyclic object motion and orientation of the sample path with respect to object motion. The information about peaks is used in a least squares framework to obtain an initial estimate of these parameters. The estimate is further refined using the full intensity profile. The best estimate for the period of cyclic object motion is obtained by looking for consensus among estimates from many sample paths. The proposed technique is evaluated with synthetic videos where ground-truth is known, and with American Sign Language videos where the goal is to detect periodic hand motions.National Science Foundation (CNS-0202067, IIS-0308213, IIS-0329009); Office of Naval Research (N00014-03-1-0108

Sparsity in Dynamics of Spontaneous Subtle Emotions: Analysis \& Application

Spontaneous subtle emotions are expressed through micro-expressions, which are tiny, sudden and short-lived dynamics of facial muscles; thus poses a great challenge for visual recognition. The abrupt but significant dynamics for the recognition task are temporally sparse while the rest, irrelevant dynamics, are temporally redundant. In this work, we analyze and enforce sparsity constrains to learn significant temporal and spectral structures while eliminate irrelevant facial dynamics of micro-expressions, which would ease the challenge in the visual recognition of spontaneous subtle emotions. The hypothesis is confirmed through experimental results of automatic spontaneous subtle emotion recognition with several sparsity levels on CASME II and SMIC, the only two publicly available spontaneous subtle emotion databases. The overall performances of the automatic subtle emotion recognition are boosted when only significant dynamics are preserved from the original sequences.Comment: IEEE Transaction of Affective Computing (2016

Analysis of Radar Doppler Signature from Human Data

This paper presents the results of time (autocorrelation) and time-frequency (spectrogram) analyses of radar signals returned from the moving human targets. When a radar signal falls on the human target which is moving toward or away from the radar, the signals reflected from different parts of his body produce a Doppler shift that is proportional to the velocity of those parts. Moving parts of the body causes the characteristic Doppler signature. The main contribution comes from the torso which causes the central Doppler frequency of target. The motion of arms and legs induces modulation on the returned radar signal and generates sidebands around the central Doppler frequency, referred to as micro-Doppler signatures. Through analyses on experimental data it was demonstrated that the human motion signature extraction is better using spectrogram. While the central Doppler frequency can be determined using the autocorrelation and the spectrogram, the extraction of the fundamental cadence frequency using the autocorrelation is unreliable when the target is in the clutter presence. It was shown that the fundamental cadence frequency increases with increasing dynamic movement of people and simultaneously the possibility of its extraction is proportional to the degree of synchronization movements of persons in the group