Reproducible Evaluation of Pan-Tilt-Zoom Tracking

Tracking with a Pan-Tilt-Zoom (PTZ) camera has been a research topic in computer vision for many years. However, it is very difficult to assess the progress that has been made on this topic because there is no standard evaluation methodology. The difficulty in evaluating PTZ tracking algorithms arises from their dynamic nature. In contrast to other forms of tracking, PTZ tracking involves both locating the target in the image and controlling the motors of the camera to aim it so that the target stays in its field of view. This type of tracking can only be performed online. In this paper, we propose a new evaluation framework based on a virtual PTZ camera. With this framework, tracking scenarios do not change for each experiment and we are able to replicate online PTZ camera control and behavior including camera positioning delays, tracker processing delays, and numerical zoom. We tested our evaluation framework with the Camshift tracker to show its viability and to establish baseline results.Comment: This is an extended version of the 2015 ICIP paper "Reproducible Evaluation of Pan-Tilt-Zoom Tracking

Evaluation of trackers for Pan-Tilt-Zoom Scenarios

Tracking with a Pan-Tilt-Zoom (PTZ) camera has been a research topic in computer vision for many years. Compared to tracking with a still camera, the images captured with a PTZ camera are highly dynamic in nature because the camera can perform large motion resulting in quickly changing capture conditions. Furthermore, tracking with a PTZ camera involves camera control to position the camera on the target. For successful tracking and camera control, the tracker must be fast enough, or has to be able to predict accurately the next position of the target. Therefore, standard benchmarks do not allow to assess properly the quality of a tracker for the PTZ scenario. In this work, we use a virtual PTZ framework to evaluate different tracking algorithms and compare their performances. We also extend the framework to add target position prediction for the next frame, accounting for camera motion and processing delays. By doing this, we can assess if predicting can make long-term tracking more robust as it may help slower algorithms for keeping the target in the field of view of the camera. Results confirm that both speed and robustness are required for tracking under the PTZ scenario.Comment: 6 pages, 2 figures, International Conference on Pattern Recognition and Artificial Intelligence 201

Deep learning-based anomalous object detection system powered by microcontroller for PTZ cameras

Automatic video surveillance systems are usually designed to detect anomalous objects being present in a scene or behaving dangerously. In order to perform adequately, they must incorporate models able to achieve accurate pattern recognition in an image, and deep learning neural networks excel at this task. However, exhaustive scan of the full image results in multiple image blocks or windows to analyze, which could make the time performance of the system very poor when implemented on low cost devices. This paper presents a system which attempts to detect abnormal moving objects within an area covered by a PTZ camera while it is panning. The decision about the block of the image to analyze is based on a mixture distribution composed of two components: a uniform probability distribution, which represents a blind random selection, and a mixture of Gaussian probability distributions. Gaussian distributions represent windows in the image where anomalous objects were detected previously and contribute to generate the next window to analyze close to those windows of interest. The system is implemented on a Raspberry Pi microcontroller-based board, which enables the design and implementation of a low-cost monitoring system that is able to perform image processing.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Eagle: End-to-end Deep Reinforcement Learning based Autonomous Control of PTZ Cameras

Existing approaches for autonomous control of pan-tilt-zoom (PTZ) cameras use multiple stages where object detection and localization are performed separately from the control of the PTZ mechanisms. These approaches require manual labels and suffer from performance bottlenecks due to error propagation across the multi-stage flow of information. The large size of object detection neural networks also makes prior solutions infeasible for real-time deployment in resource-constrained devices. We present an end-to-end deep reinforcement learning (RL) solution called Eagle to train a neural network policy that directly takes images as input to control the PTZ camera. Training reinforcement learning is cumbersome in the real world due to labeling effort, runtime environment stochasticity, and fragile experimental setups. We introduce a photo-realistic simulation framework for training and evaluation of PTZ camera control policies. Eagle achieves superior camera control performance by maintaining the object of interest close to the center of captured images at high resolution and has up to 17% more tracking duration than the state-of-the-art. Eagle policies are lightweight (90x fewer parameters than Yolo5s) and can run on embedded camera platforms such as Raspberry PI (33 FPS) and Jetson Nano (38 FPS), facilitating real-time PTZ tracking for resource-constrained environments. With domain randomization, Eagle policies trained in our simulator can be transferred directly to real-world scenarios.Comment: 20 pages, IoTD

Neural Controller for PTZ cameras based on nonpanoramic foreground detection

Abstract—In this paper a controller for PTZ cameras based on an unsupervised neural network model is presented. It takes advantage of the foreground mask generated by a nonparametric foreground detection subsystem. Thus, our aim is to optimize the movements of the PTZ camera to attain the maximum coverage of the observed scene in presence of moving objects. A growing neural gas (GNG) is applied to enhance the representation of the foreground objects. Both qualitative and quantitative results are reported using several widely used datasets, which demonstrate the suitability of our approach.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Panoramic Background Modeling for PTZ Cameras with Competitive Learning Neural Networks

The construction of a model of the background of a scene still remains as a challenging task in video surveillance systems, in particular for moving cameras. This work presents a novel approach for constructing a panoramic background model based on competitive learning neural networks and a subsequent piecewise linear interpolation by Delaunay triangulation. The approach can handle arbitrary camera directions and zooms for a Pan-Tilt-Zoom (PTZ) camera-based surveillance system. After testing the proposed approach on several indoor sequences, the results demonstrate that the proposed method is effective and suitable to use for real-time video surveillance applications.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

A robust and efficient video representation for action recognition

This paper introduces a state-of-the-art video representation and applies it to efficient action recognition and detection. We first propose to improve the popular dense trajectory features by explicit camera motion estimation. More specifically, we extract feature point matches between frames using SURF descriptors and dense optical flow. The matches are used to estimate a homography with RANSAC. To improve the robustness of homography estimation, a human detector is employed to remove outlier matches from the human body as human motion is not constrained by the camera. Trajectories consistent with the homography are considered as due to camera motion, and thus removed. We also use the homography to cancel out camera motion from the optical flow. This results in significant improvement on motion-based HOF and MBH descriptors. We further explore the recent Fisher vector as an alternative feature encoding approach to the standard bag-of-words histogram, and consider different ways to include spatial layout information in these encodings. We present a large and varied set of evaluations, considering (i) classification of short basic actions on six datasets, (ii) localization of such actions in feature-length movies, and (iii) large-scale recognition of complex events. We find that our improved trajectory features significantly outperform previous dense trajectories, and that Fisher vectors are superior to bag-of-words encodings for video recognition tasks. In all three tasks, we show substantial improvements over the state-of-the-art results

Remote Airport Traffic Control Center (2008 - 2012) Final Presentation and Workshop - Extended Abstracts

The present report contains the extended and revised version of the abstracts collection of the presentations given at the final international workshop of the DLR-project RAiCe (Remote Airport traffic Control Center, 2008 - 2012), held on November 30 2012 in Braunschweig. The RaiCe presentations are complemented by two external contributions,from the Swedish ANSP LFV and company Frequentis, representing the industrial perspective on Remote Tower research and development. The RaiCe workshop was a satellite event of the Second SESAR Innovation Days (SID 2012, Nov. 27-29) which was held in Braunschweig, following the first one in Toulouse 2011. One of the RaiCe validation results papers was presented at SID2012 and is also included in the present report for com-pleteness, besides inclusion in the SID2012 proceedings. In addition to the collection of extended abstracts and an introduction, besides some general refer-ences a list of the publications of the DLR Remote Tower Group (time frame 2002 – 2012) is provid-ed. A list of the workshop participants is added as part of the Appendix

PATELLA RESECTION IN TOTAL KNEE ARTHROPLASTY: AN ANALYTICAL COMPARISON OF THREE TECHNIQUES

Patella resection, as a routine component of TKA, can be both difficult to plan and difficult to execute. The primary purpose of this study was to evaluate the repeatability of three unique patellar resection techniques used in total knee arthroplasty. The secondary purpose of this study was to establish whether different surgical techniques were able to reproduce preoperative plans made by each surgeon. We used radiographic measurements to evaluate patellar thickness and patellar cut angle preoperatively and postoperatively. Three techniques (45 cases in total) were evaluated, revealing qualitative differences between surgical techniques and significant quantitative differences between average patellar thickness and tilt values. No one technique was found to accurately execute the preoperative plans, and all resections were completed at a more conservative thickness than was pre-planned by the surgeons. Our results reflect conclusions in the literature, finding no significance in the ability to pre-plan patellar resections