Development of a real-time full-field range imaging system

This article describes the development of a full-field range imaging system employing a high frequency amplitude modulated light source and image sensor. Depth images are produced at video frame rates in which each pixel in the image represents distance from the sensor to objects in the scene. The various hardware subsystems are described as are the details about the firmware and software implementation for processing the images in real-time. The system is flexible in that precision can be traded off for decreased acquisition time. Results are reported to illustrate this versatility for both high-speed (reduced precision) and high-precision operating modes

Tracking by Animation: Unsupervised Learning of Multi-Object Attentive Trackers

Online Multi-Object Tracking (MOT) from videos is a challenging computer vision task which has been extensively studied for decades. Most of the existing MOT algorithms are based on the Tracking-by-Detection (TBD) paradigm combined with popular machine learning approaches which largely reduce the human effort to tune algorithm parameters. However, the commonly used supervised learning approaches require the labeled data (e.g., bounding boxes), which is expensive for videos. Also, the TBD framework is usually suboptimal since it is not end-to-end, i.e., it considers the task as detection and tracking, but not jointly. To achieve both label-free and end-to-end learning of MOT, we propose a Tracking-by-Animation framework, where a differentiable neural model first tracks objects from input frames and then animates these objects into reconstructed frames. Learning is then driven by the reconstruction error through backpropagation. We further propose a Reprioritized Attentive Tracking to improve the robustness of data association. Experiments conducted on both synthetic and real video datasets show the potential of the proposed model. Our project page is publicly available at: https://github.com/zhen-he/tracking-by-animationComment: CVPR 201

Novel Texture-based Probabilistic Object Recognition and Tracking Techniques for Food Intake Analysis and Traffic Monitoring

More complex image understanding algorithms are increasingly practical in a host of emerging applications. Object tracking has value in surveillance and data farming; and object recognition has applications in surveillance, data management, and industrial automation. In this work we introduce an object recognition application in automated nutritional intake analysis and a tracking application intended for surveillance in low quality videos. Automated food recognition is useful for personal health applications as well as nutritional studies used to improve public health or inform lawmakers. We introduce a complete, end-to-end system for automated food intake measurement. Images taken by a digital camera are analyzed, plates and food are located, food type is determined by neural network, distance and angle of food is determined and 3D volume estimated, the results are cross referenced with a nutritional database, and before and after meal photos are compared to determine nutritional intake. We compare against contemporary systems and provide detailed experimental results of our system\u27s performance. Our tracking systems consider the problem of car and human tracking on potentially very low quality surveillance videos, from fixed camera or high flying \acrfull{uav}. Our agile framework switches among different simple trackers to find the most applicable tracker based on the object and video properties. Our MAPTrack is an evolution of the agile tracker that uses soft switching to optimize between multiple pertinent trackers, and tracks objects based on motion, appearance, and positional data. In both cases we provide comparisons against trackers intended for similar applications i.e., trackers that stress robustness in bad conditions, with competitive results

Assessing the time course of the influence of featural, distributional and spatial representations during reading

What does semantic similarity between two concepts mean? How could we measure it? The way in which semantic similarity is calculated might differ depending on the theoretical notion of semantic representation. In an eye-tracking reading experiment, we investigated whether two widely used semantic similarity measures (based on featural or distributional representations) have distinctive effects on sentence reading times. In other words, we explored whether these measures of semantic similarity differ qualitatively. In addition, we examined whether visually perceived spatial distance interacts with either or both of these measures. Our results showed that the effect of featural and distributional representations on reading times can differ both in direction and in its time course. Moreover, both featural and distributional information interacted with spatial distance, yet in different sentence regions and reading measures. We conclude that featural and distributional representations are distinct components of semantic representation