Measuring the Accuracy of Object Detectors and Trackers

The accuracy of object detectors and trackers is most commonly evaluated by the Intersection over Union (IoU) criterion. To date, most approaches are restricted to axis-aligned or oriented boxes and, as a consequence, many datasets are only labeled with boxes. Nevertheless, axis-aligned or oriented boxes cannot accurately capture an object's shape. To address this, a number of densely segmented datasets has started to emerge in both the object detection and the object tracking communities. However, evaluating the accuracy of object detectors and trackers that are restricted to boxes on densely segmented data is not straightforward. To close this gap, we introduce the relative Intersection over Union (rIoU) accuracy measure. The measure normalizes the IoU with the optimal box for the segmentation to generate an accuracy measure that ranges between 0 and 1 and allows a more precise measurement of accuracies. Furthermore, it enables an efficient and easy way to understand scenes and the strengths and weaknesses of an object detection or tracking approach. We display how the new measure can be efficiently calculated and present an easy-to-use evaluation framework. The framework is tested on the DAVIS and the VOT2016 segmentations and has been made available to the community.Comment: 10 pages, 7 Figure

Exploiting Flow Relationships to Improve the Performance of Distributed Applications

Application performance continues to be an issue even with increased Internet bandwidth. There are many reasons for poor application performance including unpredictable network conditions, long round trip times, inadequate transmission mechanisms, or less than optimal application designs. In this work, we propose to exploit flow relationships as a general means to improve Internet application performance. We define a relationship to exist between two flows if the flows exhibit temporal proximity within the same scope, where a scope may either be between two hosts or between two clusters of hosts. Temporal proximity can either be in parallel or near-term sequential. As part of this work, we first observe that flow relationships are plentiful and they can be exploited to improve application performance. Second, we establish a framework on possible techniques to exploit flow relationships. In this framework, we summarize the improvements that can be brought by these techniques into several types and also use a taxonomy to break Internet applications into different categories based on their traffic characteristics and performance concerns. This approach allows us to investigate how a technique helps a group of applications rather than a particular one. Finally, we investigate several specific techniques under the framework and use them to illustrate how flow relationships are exploited to achieve a variety of improvements. We propose and evaluate a list of techniques including piggybacking related domain names, data piggybacking, enhanced TCP ACKs, packet aggregation, and critical packet piggybacking. We use them as examples to show how particular flow relationships can be used to improve applications in different ways such as reducing round trips, providing better quality of information, reducing the total number of packets, and avoiding timeouts. Results show that the technique of piggybacking related domain names can significantly reduce local cache misses and also reduce the same number of domain name messages. The data piggybacking technique can provide packet-efficient throughput in the reverse direction of a TCP connection without sacrificing forward throughput. The enhanced ACK approach provides more detailed and complete information about the state of the forward direction that could be used by a TCP implementation to obtain better throughput under different network conditions. Results for packet aggregation show only a marginal gain of packet savings due to the current traffic patterns. Finally, results for critical packet piggybacking demonstrate a big potential in using related flows to send duplicate copies to protect performance-critical packets from loss

Simple Baseline for Vehicle Pose Estimation: Experimental Validation

Significant progress on human and vehicle pose estimation has been achieved in recent years. The performance of these methods has evolved from poor to remarkable in just a couple of years. This improvement has been obtained from increasingly complex architectures. In this paper, we explore the applicability of simple baseline methods by adding a few deconvolutional layers on a backbone network to estimate heat maps that correspond to the vehicle keypoints. This approach has been proven to be very effective for human pose estimation. The results are analyzed on the PASCAL3DC dataset, achieving state-of-the-art results. In addition, a set of experiments has been conducted to study current shortcomings in vehicle keypoints labelling, which adversely affect performance. A new strategy for de ning vehicle keypoints is presented and validated with our customized dataset with extended keypoints

Saliency-based cooperative landing of a multirotor aerial vehicle on an autonomous surface vehicle

This paper presents a method for vision-based landing of a multirotor unmanned aerial vehicle (UAV) on an autonomous surface vehicle (ASV) equipped with a helipad. The method includes a mechanism for helipad behavioural search when outside the UAV’s field of view, a learning saliency-based mechanism for visual tracking the helipad, and a cooperative strategy for the final vision-based landing phase. Learning how to track the helipad from above occurs during takeoff and cooperation results from having the ASV tracking the UAV for assisting its landing. A set of experimental results with both simulated and physical robots show the feasibility of the presented method.info:eu-repo/semantics/acceptedVersio

A Generic API for Load Balancing in Structured P2P Systems

International audienceReal world datasets are known to be highly skewed, often leading to an important load imbalance issue for distributed systems managing them. To address this issue, there exist almost as many load balancing strategies as there are different systems. When designing a scalable distributed system geared towards handling large amounts of information, it is often not so easy to anticipate which kind of strategy will be the most efficient to maintain adequate performance regarding response time, scalability and reliability at any time. Based on this observation, we describe the methodology behind the building of a generic API to implement and experiment any strategy independently from the rest of the code, prior to a definitive choice for instance. We then show how this API is compatible with famous existing systems and their load balancing scheme. We also present results from our own distributed system which targets the continuous storage of events structured according to the Semantic Web standards, further retrieved by interested parties. As such, our system constitutes a typical example of a Big Data environment

Monocular visual traffic surveillance: a review

To facilitate the monitoring and management of modern transportation systems, monocular visual traffic surveillance systems have been widely adopted for speed measurement, accident detection, and accident prediction. Thanks to the recent innovations in computer vision and deep learning research, the performance of visual traffic surveillance systems has been significantly improved. However, despite this success, there is a lack of survey papers that systematically review these new methods. Therefore, we conduct a systematic review of relevant studies to fill this gap and provide guidance to future studies. This paper is structured along the visual information processing pipeline that includes object detection, object tracking, and camera calibration. Moreover, we also include important applications of visual traffic surveillance systems, such as speed measurement, behavior learning, accident detection and prediction. Finally, future research directions of visual traffic surveillance systems are outlined