Detect to Track and Track to Detect

Recent approaches for high accuracy detection and tracking of object categories in video consist of complex multistage solutions that become more cumbersome each year. In this paper we propose a ConvNet architecture that jointly performs detection and tracking, solving the task in a simple and effective way. Our contributions are threefold: (i) we set up a ConvNet architecture for simultaneous detection and tracking, using a multi-task objective for frame-based object detection and across-frame track regression; (ii) we introduce correlation features that represent object co-occurrences across time to aid the ConvNet during tracking; and (iii) we link the frame level detections based on our across-frame tracklets to produce high accuracy detections at the video level. Our ConvNet architecture for spatiotemporal object detection is evaluated on the large-scale ImageNet VID dataset where it achieves state-of-the-art results. Our approach provides better single model performance than the winning method of the last ImageNet challenge while being conceptually much simpler. Finally, we show that by increasing the temporal stride we can dramatically increase the tracker speed.Comment: ICCV 2017. Code and models: https://github.com/feichtenhofer/Detect-Track Results: https://www.robots.ox.ac.uk/~vgg/research/detect-track

Learning to Detect and Track Cells for Quantitative Analysis of Time-Lapse Microscopic Image Sequences

© 2015 IEEE.Studying the behaviour of cells using time-lapse microscopic imaging requires automated processing pipelines that enable quantitative analysis of a large number of cells. We propose a pipeline based on state-of-the-art methods for background motion compensation, cell detection, and tracking which are integrated into a novel semi-automated, learning based analysis tool. Motion compensation is performed by employing an efficient nonlinear registration method based on powerful discrete graph optimisation. Robust detection and tracking of cells is based on classifier learning which only requires a small number of manual annotations. Cell motion trajectories are generated using a recent global data association method and linear programming. Our approach is robust to the presence of significant motion and imaging artifacts. Promising results are presented on different sets of in-vivo fluorescent microscopic image sequences

Experimental investigation on target detection and tracking in passive radar using long-term evolution signal

This study examines the feasibility of the recently deployed wireless communication system, namely, long-term evolution (LTE), for passive radar applications. Specifically, the capability of the system to detect and track a ground moving target is analysed. A theoretical analysis is conducted on the actual LTE signal transmitted in the atmosphere, followed by field experimentation using a moving vehicle with different bistatic geometries. The experiment examines the capability of an LTE-based passive radar to detect a moving vehicle and then track it using a standard Kalman filter. Results show that the LTE-based passive radar can detect and track the ground vehicle at different locations, speeds and trajectories, and localise it in the XY-axis coordinate

A web tool to detect and track Solar features from SDO images

Coronal bright points (CBPs) are useful features that can be used to calculate solar rotation even when no active regions are present. Unlike active regions, CBPs are dis-tributed at all latitudes on the solar disk and its lifetime varies from less than an hour to a few days. Identifying and tracking CBPs are the main keys to successfully calculate the Solar corona rotation profile for different latitudes. Over the last years this topic has been an area of research in solar astronomy and some effective methods have been developed. The purpose of this dissertation was to design a web tool that retrieves, prepro-cesses, detects and tracks CBPs on solar images and that allows search and visualization of CBPs and solar information from a database, helping astrophysicists on their solar analysis. The detection uses a gradient based segmentation algorithm that has proved to provide accurate data about CBPs’ dynamics. It was developed a website to visualize the results, hosted by SPINLab. The track-ing from 480 images confirmed to be consistent within the expected when comparing with other authors’ work. This topic was motivated by the astrophysicists need for a near to real-time tool that allows the most recent data, as well as archive with historical data, concerning the Solar corona rotation to be processed just a few minutes after the image being captured by Nasa’s Atmospheric Imaging Assembly on board of the Solar Dynamic Observatory

Real-time spatial modeling to detect and track resources on construction sites

For more than 10 years the U.S. construction industry has experienced over 1,000 fatalities annually. Many fatalities may have been prevented had the individuals and equipment involved been more aware of and alert to the physical state of the environment around them. Awareness may be improved by automatic 3D (three-dimensional) sensing and modeling of the job site environment in real-time. Existing 3D modeling approaches based on range scanning techniques are capable of modeling static objects only, and thus cannot model in real-time dynamic objects in an environment comprised of moving humans, equipment, and materials. Emerging prototype 3D video range cameras offer another alternative by facilitating affordable, wide field of view, automated static and dynamic object detection and tracking at frame rates better than 1Hz (real-time). This dissertation presents an imperical work and methodology to rapidly create a spatial model of construction sites and in particular to detect, model, and track the position, dimension, direction, and velocity of static and moving project resources in real-time, based on range data obtained from a three-dimensional video range camera in a static or moving position. Existing construction site 3D modeling approaches based on optical range sensing technologies (laser scanners, rangefinders, etc.) and 3D modeling approaches (dense, sparse, etc.) that offered potential solutions for this research are reviewed. The choice of an emerging sensing tool and preliminary experiments with this prototype sensing technology are discussed. These findings led to the development of a range data processing algorithm based on three-dimensional occupancy grids which is demonstrated in detail. Testing and validation of the proposed algorithms have been conducted to quantify the performance of sensor and algorithm through extensive experimentation involving static and moving objects. Experiments in indoor laboratory and outdoor construction environments have been conducted with construction resources such as humans, equipment, materials, or structures to verify the accuracy of the occupancy grid modeling approach. Results show that modeling objects and measuring their position, dimension, direction, and speed had an accuracy level compatible to the requirements of active safety features for construction. Results demonstrate that video rate 3D data acquisition and analysis of construction environments can support effective detection, tracking, and convex hull modeling of objects. Exploiting rapidly generated three-dimensional models for improved visualization, communications, and process control has inherent value, broad application, and potential impact, e.g. as-built vs. as-planned comparison, condition assessment, maintenance, operations, and construction activities control. In combination with effective management practices, this sensing approach has the potential to assist equipment operators to avoid incidents that result in reduce human injury, death, or collateral damage on construction sites.Civil, Architectural, and Environmental Engineerin

Space debris measurement program at Phillips Laboratory

Ground-based optical sensing was identified as a technique for measuring space debris complementary to radar in the critical debris size range of 1 to 10 cm. The Phillips Laboratory is building a staring optical sensor for space debris measurement and considering search and track optical measurement at additional sites. The staring sensor is implemented in collaboration with Wright Laboratory using the 2.5 m telescope at Wright Patterson AFB, Dayton, Ohio. The search and track sensor is designed to detect and track orbital debris in tasked orbits. A progress report and a discussion of sensor performance and search and track strategies will be given

Penalty Dynamic Programming Algorithm for Dim Targets Detection in Sensor Systems

In order to detect and track multiple maneuvering dim targets in sensor systems, an improved dynamic programming track-before-detect algorithm (DP-TBD) called penalty DP-TBD (PDP-TBD) is proposed. The performances of tracking techniques are used as a feedback to the detection part. The feedback is constructed by a penalty term in the merit function, and the penalty term is a function of the possible target state estimation, which can be obtained by the tracking methods. With this feedback, the algorithm combines traditional tracking techniques with DP-TBD and it can be applied to simultaneously detect and track maneuvering dim targets. Meanwhile, a reasonable constraint that a sensor measurement can originate from one target or clutter is proposed to minimize track separation. Thus, the algorithm can be used in the multi-target situation with unknown target numbers. The efficiency and advantages of PDP-TBD compared with two existing methods are demonstrated by several simulations