Hungarian Assignment Algorithm Research Articles

Joint PHD Filter and Hungarian Assignment Algorithm for Multitarget Tracking in Low Signal-to-Noise Ratio

Joint PHD Filter and Hungarian Assignment Algorithm for Multitarget Tracking in Low Signal-to-Noise Ratio

Quay-Yard operation analysis of port container terminals in Nigeria

This paper presents significant measures to tackling berth occupancy and quay-yard operation problems. Case study was conducted on a port container terminal in Nigeria confidentially identified as ‘Port A’ in this paper. Hungarian assignment algorithm was used to reduce the total travel time of the yard trucks from an average of 6.4 minutes to 3.5minutes, while M/M/c/K queuing model was applied in scheduling the quay crane assignments. Results showed tremendous increase in quay crane rate, berth capacity and improved berth utilization. Also, for the reach stacker, the crane rate can be improved to 30 TEUs/hr at berth occupancy below 0.40 with TEUs factor of 1.4. These results imply that for Nigeria terminals operating with reach stacker crane, it is possible to improve the berth capacity without affecting the berth utilization of the terminal.

Distributed Optimization Algorithm for Multi-Robot Formation with Virtual Reference Center

In this letter, we use a distributed optimization approach to solve a class of multi-robot formation problem with virtual reference center. We investigate the design and analysis of the constrained consensus algorithm to solve the optimization problem with a sum of objective functions with some local constraints. In the multi-robot system with virtual reference center, each robot has messages on its own constraints and objective function, as well as the message about the formation that interacts with the virtual reference center. At the same time, all the robots collaborate to find the minimum value of the function defined by the formation. To find the optimal formation, we propose an algorithm with fixed step size with better performance. In addition, we use a combination of the Hungarian assignment algorithm and the proposed formation algorithm to get the optimal matching formation of the multi-robot system.

Application of clustering and the Hungarian algorithm to the problem of consistent vortex tracking in incompressible flowfields

The ability to track vortices spatially and temporally is of great interest for the study of complex and turbulent flows. A methodology to solve the problem of vortex tracking by the application of machine learning approaches is investigated. First a well-known vortex detection algorithm is applied to identify coherent structures. Hierarchical clustering is then conducted followed by a unique application of the Hungarian assignment algorithm. Application to a synthetic flowfield of merging Batchelor vortices results in robust vortex labelling even in a vortex merging event. A robotic PIV experimental dataset of a canonical Ahmed body is used to demonstrate the applicability of the method to three-dimensional flows.Graphic abstract

Active Player Detection in Handball Scenes Based on Activity Measures.

In team sports training scenes, it is common to have many players on the court, each with his own ball performing different actions. Our goal is to detect all players in the handball court and determine the most active player who performs the given handball technique. This is a very challenging task, for which, apart from an accurate object detector, which is able to deal with complex cluttered scenes, additional information is needed to determine the active player. We propose an active player detection method that combines the Yolo object detector, activity measures, and tracking methods to detect and track active players in time. Different ways of computing player activity were considered and three activity measures are proposed based on optical flow, spatiotemporal interest points, and convolutional neural networks. For tracking, we consider the use of the Hungarian assignment algorithm and the more complex Deep SORT tracker that uses additional visual appearance features to assist the assignment process. We have proposed the evaluation measure to evaluate the performance of the proposed active player detection method. The method is successfully tested on a custom handball video dataset that was acquired in the wild and on basketball video sequences. The results are commented on and some of the typical cases and issues are shown.

Development of a N-type GM-PHD filter for multiple target, multiple type visual tracking

We propose a new framework that extends the standard Probability Hypothesis Density (PHD) filter for multiple targets having N⩾2 different types based on Random Finite Set theory, taking into account not only background clutter, but also confusions among detections of different target types, which are in general different in character from background clutter. Under Gaussianity and linearity assumptions, our framework extends the existing Gaussian mixture (GM) implementation of the standard PHD filter to create a N-type GM-PHD filter. The methodology is applied to real video sequences by integrating object detectors’ information into this filter for two scenarios. For both cases, Munkres’s variant of the Hungarian assignment algorithm is used to associate tracked target identities between frames. This approach is evaluated and compared to both raw detection and independent GM-PHD filters using the Optimal Sub-pattern Assignment metric and discrimination rate. This shows the improved performance of our strategy on real video sequences.

Visual Persons Behavior Diary Generation Model based on Trajectories and Pose Estimation

The behavior pattern of persons was the important output of the surveillance analysis. This paper focus on the generation model of visual person behavior diary. The pipeline includes the person detection, tracking, and the person behavior classify. This paper adopts the deep convolutional neural model YOLO (You Only Look Once)V2 for person detection module. Multi person tracking was based on the detection framework. The Hungarian assignment algorithm was used to the matching. The person appearance model was integrated by HSV color model and Hash code model. The person object motion was estimated by the Kalman Filter. The multi objects were matching with exist tracklets through the appearance and motion location distance by the Hungarian assignment method. A long continuous trajectory for one person was get by the spatial-temporal continual linking algorithm. And the face recognition information was used to identify the trajectory. The trajectories with identification information can be used to generate the visual diary of person behavior based on the scene context information and person action estimation. The relevant modules are tested in public data sets and our own capture video sets. The test results show that the method can be used to generate the visual person behavior pattern diary with certain accuracy.

Improvement of Honey Bee Tracking on 2D Video with Hough Transform and Kalman Filter

This paper introduces a novel approach to improve bee tracking. For this research, 50 Hz 2D videos have been recorded. The first part of the model tracks bees using a method combining colour thresholding and foreground detection, and then produces the bees’ shapes as blobs on a binary image. These blobs are analysed to estimate the positions and directions of motion of the bees. However, when bees cross over one another in the image, they are hard to track. This paper tackles this problem by using the standard Hough transform applied to bee research. Then a Kalman filter is used to track the bees using their estimated position information. Because of the 50 Hz frame rate, the trajectories of the bee movements are too variable to track reliably. The Kalman filter is modified to fit this situation. Multiple bees are being tracked, so the Hungarian assignment algorithm is used to assign predictions and measurements to individual bees. The experiment shows the bees are reliably tracked in the close view 50 Hz 2D video.

Restricted set classification: Who is there?

We consider a problem where a set X of N objects (instances) coming from c classes have to be classified simultaneously. A restriction is imposed on X in that the maximum possible number of objects from each class is known, hence we dubbed the problem who-is-there? We compare three approaches to this problem: (1) independent classification whereby each object is labelled in the class with the largest posterior probability; (2) a greedy approach which enforces the restriction; and (3) a theoretical approach which, in addition, maximises the likelihood of the label assignment, implemented through the Hungarian assignment algorithm. Our experimental study consists of two parts. The first part includes a custom-made chess data set where the pieces on the chess board must be recognised together from an image of the board. In the second part, we simulate the restricted set classification scenario using 96 datasets from a recently collated repository (University of Santiago de Compostela, USC). Our results show that the proposed approach (3) outperforms approaches (1) and (2).

Full-class set classification using the Hungarian algorithm

Consider a set-classification task where c objects must be labelled simultaneously in c classes, knowing that there is only one object coming from each class (full-class set). Such problems may occur in automatic attendance registration systems, simultaneous tracking of fast moving objects and more. A Bayes-optimal solution to the full-class set classification problem is proposed using a single classifier and the Hungarian assignment algorithm. The advantage of set classification over individually based classification is demonstrated both theoretically and experimentally, using simulated, benchmark and real data.

Improving the Hungarian assignment algorithm

We describe three easily implementable improvement for the Hungarian linear assignment algorithm. Computation times vary from about two to more than three times lower than previously, where the effectiveness increases with problem size. Furthermore, the algorithm is now less sensitive to the range of the cost coefficients. We also show that the Hungarian algorithm is essentially equivalent to assignment algorithms based on shortest augmenting paths.

Letter to the Editor—Application of Kuhn's Hungarian Assignment Algorithm to Posting Servicemen

This communication describes the use of Kuhn's Hungarian assignment algorithm for deriving postings for servicemen. Test computer runs give favorable results.