Player detection in field sports

Download

index.pdf

Date

2018-02-01

Author

Direkoglu, Cem
Sah, Melike
O'Connor, Noel E.

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

189
views

87
downloads

We describe a method for player detection in field sports with a fixed camera setup based on a new player feature extraction strategy. The proposed method detects players in static images with a sliding window technique. First, we compute a binary edge image and then the detector window is shifted over the edge regions. Given a set of binary edges in a sliding window, we introduce and solve a particular diffusion equation to generate a shape information image. The proposed diffusion to generate a shape information image is the key stage and the main theoretical contribution in our new algorithm. It removes the appearance variations of an object while preserving the shape information. It also enables the use of polar and Fourier transforms in the next stage to achieve scale- and rotation-invariant feature extraction. A support vector machine classifier is used to assign either player or non-player class inside a detector window. We evaluate our approach on three different field hockey datasets. In general, results show that the proposed feature extraction is effective and performs competitive results compared to the state-of-the-art methods.

Subject Keywords

Feature extraction, Heat diffusion, Player detection, Field sports

URI

https://hdl.handle.net/11511/67283

Journal

MACHINE VISION AND APPLICATIONS

DOI

https://doi.org/10.1007/s00138-017-0893-8

Collections

Engineering, Article

Suggestions

OpenMETU
Core

Evaluation of Image Representations for Player Detection in Field Sports Using Convolutional Neural Networks Sah, Melike; Direkoglu, Cem (2018-08-28) Player detection is an important task in sport video analysis. Once players are detected accurately, it can be used for player tracking, player activity/performance analysis as well as team activity recognition. Recently, convolutional Neural Networks (CNN) became the state-of-the-art in computer vision for object recognition. CNN based methods usually use gray or RGB images as an input. It is also possible to use other image representation techniques such as shape information image and polar transformed sh...
Feature Detection and Tracking for Extraction of Crowd Dynamics Gunduz, Ayse Elvan; Temizel, Alptekin; Temizel, Tugba Taskaya (2013-01-01) Extraction of crowd dynamics from video is the fundamental step for automatic detection of abnormal events. However, it is difficult to obtain sufficient performance with object tracking due to occlusions and insufficient resolution of the objects in the scene. As a result, optical flow or feature tracking methods are preferred in crowd videos. These applications also require algorithms to work in real-time. In this work, we investigated the applicability and performance of feature detection and tracking al...
Expectation Maximization-Based Detection in Range-Heterogeneous Weibull Clutter Doyuran, Ulku Cilek; Tanık, Yalçın (2014-10-01) The problem of radar target detection in Weibull and range-heterogeneous clutter is considered. The clutter component in each range cell is modeled as an instance of a random variable belonging to one of several Weibull distributions. We use the expectation-maximization algorithm to estimate parameters of the distribution and set the threshold accordingly. The performance of the proposed receiver is investigated through computer simulations and observed to be superior compared with numerous well-known methods.
Investigation of the Effects of Buried Object Orientation in Subsurface Target Detection by GPR Doğan, Mesut; Sayan, Gönül (2017-10-27) Use of ultra-widehand ground penetrating radars (GPRs) have been the most effective approach in subsurface target detection. Proper preprocessing, prescreening, feature extraction, classification and fusion techniques are all needed to satisfy the requirements of high detection probability and low false alarm rates, simultaneously. Dominating ground reflection signals and soil clutter effects including the randomly varying non-planar ground surfaces, inhomogeneous soil nature, pebbles or rocks within the so...
A Radar Target Recognition Method with MUSIC Algorithm: Application to Aircraft Targets with Measured Scattered Data Secmen, M.; Turhan-Sayan, G.; Sayan, Gönül (2008-05-30) This paper demonstrates the usefulness of an ultra wideband target recognition method in the case of realistic and complicated target geometries at resonance region. The method utilizes the MUSIC algorithm to extract the natural resonance-related scattering features of targets. The resulting features give the power distribution maps of targets. These maps are called as fused MUSIC spectrum matrices and used as the main target recognition feature in the method. The fusion process is needed to reduce the aspe...

Citation Formats

C. Direkoglu, M. Sah, and N. E. O’Connor, “Player detection in field sports,” MACHINE VISION AND APPLICATIONS, pp. 187–206, 2018, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/67283.