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In this work we propose a method for classification of sports types from combined audio and visual features extracted from thermal video. From audio Mel Frequency Cepstral Coefficients (MFCC) are extracted, and PCA are applied to reduce the feature space to 10 dimensions. From the visual modality short trajectories are constructed to represent the motion of players. From these, four motion features...
The detect and track of the weak target in Bearing-Time Record (BTR) was studied in this paper. The distribution of the measurements of the BTR was derived. And a method of track before detect algorithm based on Hidden Markov Model was given. The simulation and the experiment with real acoustic data showed the effectiveness of this method. The performance of the two classic algorithms Viterbi and...
In this paper, we present a motion segmentation based robust multi-target tracking technique for on-road obstacles. Our approach uses depth imaging information, and integrates persistence topology for segmentation and min-max network flow for tracking. To reduce time as well as computational complexity, the max flow problem is solved using a dynamic programming algorithm. We classify the sensor reading...
In external beam radiotherapy (EBRT), one of the major challenges is to compensate for target motion induced by patient's respiration and organ motion. The accurate delivery of radiation to the moving targets (tumors) requires new innovative technologies and methods to avoid excessive irradiation of healthy tissue. Recently, we developed a tracking method for target motion compensations, which includes...
We propose a visual tracking method for dense fish schools in which occlusions occur frequently. Although much progress has been made for tracking multiple objects in video images, it is challenging to track individuals in highly dense groups. For occluded fishes, estimation of their positions and directions is difficult. However, if we know the number of fishes in a local area, we can accurately...
Accurate multi-person tracking under complex conditions is an important topic in computer vision with various application scenarios such as visual surveillance. Taking into account the difficulties caused by 2D occlusions, missing detections, and false positives, we propose a two-stage graph-based object tracking-by-detection approach using multiple calibrated cameras. Firstly, data association is...
In tracking closely located multiple targets, the traditional optimal multitarget data association approach such as joint integrated probabilistic data association (JIPDA) faces exponential complexity caused by combinatorial increasing of the number of possible measurement-to-track allocations, which severely limits its applicability. This paper presents an iterative implementation of the Joint Integrated...
The Mu2e experiment at Fermilab will search for the coherent μ→ e conversion on Al. The expected Mu2e single event sensitivity is 2.5 · 10 17, an improvement of a factor 10 over the present limit. Mn2e takes advantage of a high intensity muon beam with more than 1010 muons stopping in the target every second. The detector system is composed of a low-mass straw tracker and an crystal calorimeter. Efficient...
The existing simulated mouses are mostly based on the hand tracking technique, which depend on the data gloves, remote controllers or other equipment, leading to high costs and severe user limits. In this paper, we develop a new kind of simulated mouses by employing dynamic hand gesture recognition in which the palm node is detected by a Kinect camera. According to the movements of users in real world,...
We attack the problem of persistently tracking cooperative people such as children, the elderly or patients by combining passive tracking and active tracking techniques. Passive tracking uses visual signals from surveillance cameras, but vision based people tracking becomes a hard problem in challenging scenarios such as long-term/heavy occlusion, people changing their movement patterns during occlusion,...
Dynamic programming based track before detect (DP-TBD) is a batch processing method. It exploits the space-time correlation among several consecutive frames of measurements and jointly processes all the measurements in these frames. Due to this batch processing manner, DP-TBD suffers heavy computational load since it involves processing a large volume of data. Moreover, in order to track long continuous...
This paper introduces the Langevin Monte Carlo Filter (LMCF), a particle filter with a Markov chain Monte Carlo algorithm which draws proposals by simulating Hamiltonian dynamics. This approach is well suited to non-linear filtering problems in high dimensional state spaces where the bootstrap filter requires an impracticably large number of particles. The simulation of Hamiltonian dynamics is motivated...
In recent studies of multi-target tracking, highorder association and its corresponding high-order affinity (or similarity) is often preferred over pairwise comparisons to capture high-order discriminative information. A naturally raised challenge is to calculate affinity (or similarity) among more than two target candidates. When target appearance is represented by histograms, such as the popular...
In this paper, we propose a method for optimal stochastic sensor control, where the goal is to minimise the estimation error in multi-object tracking scenarios. Our approach is based on an information theoretic divergence measure between labelled random finite set densities. The multi-target posteriors are generalised labelled multi-Bernoulli (GLMB) densities, which do not permit closed form solutions...
Fundamental to any state estimation problem is the concept of estimation error. In both autonomous robotics and tracking research, the ability to assess the performance of robotic mapping and target tracking algorithms is of crucial importance. This article focusses on metrics for the automatic evaluation of target tracking and feature map estimation algorithms, in the presence of both detection and...
This paper proposes a new implementation for the delta generalized labeled multi-Bernoulli (δ-GLMB) filter by combining prediction and update into a single step. In contrast to the original implementation which requires different truncation procedures for each component in the prediction and update, the joint strategy involves only one truncation per component in the filtering density, thus drastically...
In this paper, we consider the problem of guarding a valuable naval asset from a highly maneuverable threat via the use of autonomous unmanned surface vehicles (USVs) as dynamic obstacles. The objective of the defending agent is to maximize the amount of time it takes an intruder boat to enter the restricted area. Here we introduce a set of active blocking strategies which allow the defender to influence...
In this paper, a control law for three dimensional trajectory tracking for unmanned aerial vehicles is developed. The control law is based on back stepping method. The parameters of the control law are tuned by genetic algorithm. Simulations show that the effectiveness of the control law and the parameter tuning method. The proposed control law has good performance in the presence of time-varying...
Conventional experiments on MTT are built upon the belief that fixing the detections to different trackers is sufficient to obtain a fair comparison. In this work we argue how the true behavior of a tracker is exposed when evaluated by varying the input detections rather than by fixing them. We propose a systematic and reproducible protocol and a MATLAB toolbox for generating synthetic data starting...
High-resolution images can be used to resolve matching ambiguities between trajectory fragments (tracklets), which is one of the main challenges in multiple target tracking. A PTZ camera, which can pan, tilt and zoom, is a powerful and efficient tool that offers both close-up views and wide area coverage on demand. The wide-area view makes it possible to track many targets while the close-up view...
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