Micro-expressions play an essential part in understanding non-verbal communication and deceit detection. They are involuntary, brief facial movements that are shown when a person is trying to conceal something. Automatic analysis of micro-expression is challenging due to their low amplitude and to their short duration (they occur as fast as 1/15 to 1/25 of a second). We propose a fully micro-expression analysis system consisting of a high-speed image acquisition setup and a software framework which can detect the frames when the micro-expressions occurred as well as determine the type of the emerged expression. The detection and classification methods use fast and simple motion descriptors based on absolute image differences. The recognition module it only involves the computation of several 2D Gaussian probabilities. The software framework was tested on two publicly available high speed micro-expression databases and the whole system was used to acquire new data. The experiments we performed show that our solution outperforms state of the art works which use more complex and computationally intensive descriptors.

Deceit occurs in daily life and, even from an early age, children can successfully deceive their parents. Therefore, numerous book and psychological studies have been published to help people decipher the facial cues to deceit. In this study, we tackle the problem of deceit detection by analyzing eye movements: blinks, saccades and gaze direction. Recent psychological studies have shown that the non-visual saccadic eye movement rate is higher when people lie. We propose a fast and accurate framework for eye tracking and eye movement recognition and analysis. The proposed system tracks the position of the iris, as well as the eye corners (the outer shape of the eye). Next, in an offline analysis stage, the trajectory of these eye features is analyzed in order to recognize and measure various cues which can be used as an indicator of deception: the blink rate, the gaze direction and the saccadic eye movement rate. On the task of iris center localization, the method achieves within pupil localization in 91.47% of the cases. For blink localization, we obtained an accuracy of 99.3% on the difficult EyeBlink8 dataset. In addition, we proposed a novel metric, the normalized blink rate deviation to stop deceitful behavior based on blink rate. Using this metric and a simple decision stump, the deceitful answers from the Silesian Face database were recognized with an accuracy of 96.15%.

Camera calibration is essential for accurate computer vision, and automatic calibration of some extrinsic parameters is needed in case the camera is placed on a mobile platform. The pitch and yaw angles, which are the most likely ones to change as the vehicle moves, can be inferred from the image coordinates of the vanishing point (VP). In this paper we present an artificial neural network approach for detecting the vanishing point position in road traffic scenarios. The network is trained using 2500 images which are first automatically annotated using a classical vanishing point detection algorithm, and then manually validated. The training and test datasets are made publicly available. The trained network was tested on more than 250 images not previously seen by the network, locating the VP accurately in more than 90% of the cases.

The low-Earth orbit (LEO) is filled with active satellites, but also with space debris, which need constant observation. The orbiting objects may be affected by collisions or by atmospheric drag, and therefore they can change their orbit or even fall to the ground, a process known as reentry. The low altitude of these objects (below 2000 km, usually even below 1000 km) means that at given time they can be observed from a limited range of locations on the Earths’ surface, and therefore having multiple, easy to set up observation stations can be extremely useful. This paper presents a portable hardware solution for on-demand wide-field surveillance of the LEO region, the image processing algorithms for detecting the satellite streaks and for joining these streaks into tracklets, and the solution for astrometrical reduction and generating the result file for each tracklet. An automatic validation solution that is able to automatically identify the detected satellites and compute the measurement angular errors is also presented. The acquisition and processing system is built with commercially available items of low and moderate costs and is capable of on-site acquisition and real-time processing of images. The acquired images are processed by background subtraction, analysis of the difference between frames, extraction of elongated objects corresponding to the satellite streaks, and forming trajectories (tracklets) from consecutive detections. The pixel coordinates of the tracklets are converted to angular coordinates using the tools from Astrometry.net, subsequently filtered for improving the accuracy. The results are validated by using daily updated orbital parameters (TLEs), which are used to predict the angular positions that are subsequently matched with the detection results.

Ego-vehicle state prediction represents a complex and challenging problem for self-driving and autonomous vehicles. Sensorial information and on-board cameras are used in perception-based solutions in order to understand the state of the vehicle and the surrounding traffic conditions. Monocular camera-based methods are becoming increasingly popular for driver assistance, with precise predictions of vehicle speed and emergency braking being important for road safety enhancement, especially in the prevention of speed-related accidents. In this research paper, we introduce the implementation of a convolutional neural network (CNN) model tailored for the prediction of vehicle velocity, braking events, and emergency braking, employing sequential image sequences and velocity data as inputs. The CNN model is trained on a dataset featuring sequences of 20 consecutive images and corresponding velocity values, all obtained from a moving vehicle navigating through road-traffic scenarios. The model’s primary objective is to predict the current vehicle speed, braking actions, and the occurrence of an emergency-brake situation using the information encoded in the preceding 20 frames. We subject our proposed model to an evaluation on a dataset using regression and classification metrics, and comparative analysis with existing published work based on recurrent neural networks (RNNs). Through our efforts to improve the prediction accuracy for velocity, braking behavior, and emergency-brake events, we make a substantial contribution to improving road safety and offer valuable insights for the development of perception-based techniques in the field of autonomous vehicles.

Cameras are sensors that are available anywhere and to everyone, and can be placed easily inside vehicles. While stereovision setups of two or more synchronized cameras have the advantage of directly extracting 3D information, a single camera can be easily set up behind the windshield (like a dashcam), or above the dashboard, usually as an internal camera of a mobile phone placed there for navigation assistance. This paper presents a framework for extracting and tracking obstacle 3D data from the surrounding environment of a vehicle in traffic, using as a sensor a generic camera. The system combines the strength of Convolutional Neural Network (CNN)-based segmentation with a generic probabilistic model of the environment, the dynamic occupancy grid. The main contributions presented in this paper are the following: A method for generating the probabilistic measurement model from monocular images, based on CNN segmentation, which takes into account the particularities, uncertainties, and limitations of monocular vision; a method for automatic calibration of the extrinsic and intrinsic parameters of the camera, without the need of user assistance; the integration of automatic calibration and measurement model generation into a scene tracking system that is able to work with any camera to perceive the obstacles in real traffic. The presented system can be easily fitted to any vehicle, working standalone or together with other sensors, to enhance the environment perception capabilities and improve the traffic safety.