Human activity tracking from moving camera stereo data

We present a method for tracking human activity using observations from a moving narrow-baseline stereo camera. Range data are computed from the disparity between stereo image pairs. We propose a novel technique for calculating weighting scores from range data given body configuration hypotheses. We use a modified Annealed Particle Filter to recover the optimal tracking candidate from a low dimensional latent space computed from motion capture data and constrained by an activity model. We evaluate the method on synthetic data and on a walking sequence recorded using a moving hand-held stereo camera

RGBD Datasets: Past, Present and Future

Since the launch of the Microsoft Kinect, scores of RGBD datasets have been released. These have propelled advances in areas from reconstruction to gesture recognition. In this paper we explore the field, reviewing datasets across eight categories: semantics, object pose estimation, camera tracking, scene reconstruction, object tracking, human actions, faces and identification. By extracting relevant information in each category we help researchers to find appropriate data for their needs, and we consider which datasets have succeeded in driving computer vision forward and why. Finally, we examine the future of RGBD datasets. We identify key areas which are currently underexplored, and suggest that future directions may include synthetic data and dense reconstructions of static and dynamic scenes.Comment: 8 pages excluding references (CVPR style

Robust pedestrian detection and tracking in crowded scenes

In this paper, a robust computer vision approach to detecting and tracking pedestrians in unconstrained crowded scenes is presented. Pedestrian detection is performed via a 3D clustering process within a region-growing framework. The clustering process avoids using hard thresholds by using bio-metrically inspired constraints and a number of plan view statistics. Pedestrian tracking is achieved by formulating the track matching process as a weighted bipartite graph and using a Weighted Maximum Cardinality Matching scheme. The approach is evaluated using both indoor and outdoor sequences, captured using a variety of different camera placements and orientations, that feature significant challenges in terms of the number of pedestrians present, their interactions and scene lighting conditions. The evaluation is performed against a manually generated groundtruth for all sequences. Results point to the extremely accurate performance of the proposed approach in all cases

Event-based Vision: A Survey

Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes. Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as low-latency, high speed, and high dynamic range. However, novel methods are required to process the unconventional output of these sensors in order to unlock their potential. This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras. We present event cameras from their working principle, the actual sensors that are available and the tasks that they have been used for, from low-level vision (feature detection and tracking, optic flow, etc.) to high-level vision (reconstruction, segmentation, recognition). We also discuss the techniques developed to process events, including learning-based techniques, as well as specialized processors for these novel sensors, such as spiking neural networks. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with the world

Survey on Vision-based Path Prediction

Path prediction is a fundamental task for estimating how pedestrians or vehicles are going to move in a scene. Because path prediction as a task of computer vision uses video as input, various information used for prediction, such as the environment surrounding the target and the internal state of the target, need to be estimated from the video in addition to predicting paths. Many prediction approaches that include understanding the environment and the internal state have been proposed. In this survey, we systematically summarize methods of path prediction that take video as input and and extract features from the video. Moreover, we introduce datasets used to evaluate path prediction methods quantitatively.Comment: DAPI 201

Bio-Inspired Stereo Vision Calibration for Dynamic Vision Sensors

Many advances have been made in the eld of computer vision. Several recent research trends have focused on mimicking human vision by using a stereo vision system. In multi-camera systems, a calibration process is usually implemented to improve the results accuracy. However, these systems generate a large amount of data to be processed; therefore, a powerful computer is required and, in many cases, this cannot be done in real time. Neuromorphic Engineering attempts to create bio-inspired systems that mimic the information processing that takes place in the human brain. This information is encoded using pulses (or spikes) and the generated systems are much simpler (in computational operations and resources), which allows them to perform similar tasks with much lower power consumption, thus these processes can be developed over specialized hardware with real-time processing. In this work, a bio-inspired stereovision system is presented, where a calibration mechanism for this system is implemented and evaluated using several tests. The result is a novel calibration technique for a neuromorphic stereo vision system, implemented over specialized hardware (FPGA - Field-Programmable Gate Array), which allows obtaining reduced latencies on hardware implementation for stand-alone systems, and working in real time.Ministerio de Economía y Competitividad TEC2016-77785-PMinisterio de Economía y Competitividad TIN2016-80644-