The results come from three 60-second football video sequences and do not establish the system as a replacement for match officials. The study focused on automatic identification and tracking of players and the ball in soccer video. Its offside framework compares attacking-player positions with the ball and the offside line associated with the second-placed defending player.

The researchers followed the 2023 FIFA rule described in the paper, under which the determination considers the player’s head, torso and feet but not the arms. The system was tested in three deliberately contrasting scenes: a rapid attack-to-defense transition in daylight, an evening sequence with gradually dimming light and shadows, and a brightly illuminated night counterattack with high background contrast and slight camera shake. At the center of the detection method is a five-component Gaussian mixture model, a background-subtraction technique that represents the changing appearance of each pixel with multiple Gaussian distributions.

The researchers set the number of components to five to balance the difficulty of modeling complex football backgrounds against computational cost. Conventional GMM systems can generate false detections, missed foreground regions and broken edges when illumination changes or moving background elements interfere. A fixed learning rate creates a second problem: if updates are too slow, the model cannot adapt to environmental changes and may leave ghost traces around fast-moving players; if updates are too rapid, slowly moving objects can be incorporated into the background.

The proposed GMM adjusts its learning rate using both the stage of model construction and measured changes in the scene. During the first 100 frames, the initial rate of 0.10 is increased gradually to accelerate convergence and suppress early interference. In later frames, the base rate is 0.05 and the minimum permitted value is 0.02.

The system estimates illumination change from the mean normalized grayscale difference between the current frame and the background model. When this measure exceeds 0.15, the background update is accelerated; when illumination stabilizes, the rate returns toward its base value. In a sensitivity analysis, the improved model at a rate of 0.06 produced 90% accuracy, 87% recall and an 89% F1 score, while the corresponding fixed-rate GMM began to deteriorate as the rate increased.

To recover motion regions more completely, the researchers used four-frame differencing rather than conventional three-frame differencing. Four consecutive frames yield three binary difference images. The system combines their union, representing all detected inter-frame changes, with their intersection, representing changes visible across all three comparisons.

Each difference image is binarized using an Otsu-derived threshold plus a correction based on the image’s grayscale standard deviation; the light-suppression coefficient for that correction is 0.5. When the analysis window increased from three to four frames, average detection accuracy rose by 4.7% in the daylight scene, 7.3% in the dimming evening scene and 9.3% in the night counterattack. False-detection rates fell by 3.9%, 4.3% and 4.9%, respectively.

Adding fifth and sixth frames produced less than 0.5% additional accuracy in each scene, while the false-detection rate tended to saturate and per-frame processing time increased. The four-frame configuration was therefore selected as the balance between detection performance and computational efficiency. The paper also fuses Canny edge information with the motion mask to address holes and incomplete outlines.

The upper Canny threshold is selected adaptively through Otsu’s method, and edges near the foreground are isolated by intersecting them with a foreground mask dilated using a 3-by-3 kernel. In the ablation study, four-frame differencing contributed about 7.1% to average accuracy relative to three-frame differencing, while Canny fusion added about 1.0%. The full detection configuration reached 95.4% average accuracy, compared with 85.9% for traditional GMM with three-frame differencing.

Detection supplies positions in individual frames, so the study added tracking to preserve each target’s identity through time. The baseline was Fast Discriminant Scale Space Tracking, or FDSST, which learns a multichannel correlation filter and locates a target in later frames by finding the strongest response. Principal component analysis reduces the dimensionality of the features used by the filter, while incremental updates allow the model to adapt to the video sequence.

The researchers selected FDSST because it can adjust to changes in target scale without requiring a separate deep re-identification network, additional training data or large annotated datasets. In comparisons over frame positions from 5 to 225, the improved GMM-FFD detector ranged from 85.4% to 99.3% accuracy, versus maximum accuracies of 83.7% for ViBe, 82.9% for GMM and 59.7% for kernel density estimation. Severe occlusion remains difficult for correlation-filter tracking because the response map may develop several peaks or its strongest peak may fall sharply, causing drift or loss.

The modified FDSST therefore introduces a Kalman-filter fallback governed by a confidence score derived from the maximum, mean and standard deviation of the response map. Above 0.65, the FDSST estimate is treated as reliable; below 0.35, the system switches to the Kalman filter and predicts the target from its horizontal and vertical positions and velocities. Within that interval, it retains the mode used in the previous frame.

In the occlusion test, mean center-point error was 20.2 pixels for the dynamically switched tracker, compared with 29.3 pixels for baseline FDSST and 26.0 pixels when the Kalman filter was always active. The improved tracker’s accuracy curve stabilized at 86.7%, versus 73.4% for the unmodified version. The experimental protocol separated parameter tuning from testing.

The three ISSIA-CNR sequences, each 60 seconds long, contained about 4,500 frames at 1920 by 1080 pixels and 25 frames per second. Two experts manually annotated player bounding boxes and offside lines, and their agreement was evaluated with a Kappa coefficient of 0.92. For each sequence, the first 70% of the frames were used for grid-search parameter tuning and the final 30% for testing.

Evaluation covered average accuracy, false-positive rate and frames per second, with results averaged across three runs. Tracking error was sampled every 40 frames. SoccerNet was used only for cross-dataset validation, while comparison methods followed the same experimental protocol.

Against deep-learning detectors under the reported setup, the hybrid detector processed 35 frames per second on ISSIA-CNR, compared with 23 frames per second for YOLOv5s and 12 frames per second for Faster R-CNN. Its mAP@0.5 of 92.9% was below the reported 93.1% for Faster R-CNN but above 91.6% for YOLOv5s and 92.4% for YOLOv8s. The complete tracking system ran at 35 frames per second with a reported 68.4% Multiple Object Tracking Accuracy and 28 identity switches.

The method has 59.41 million parameters and still requires substantial computing resources. Heavy rain is another stated limitation: rain streaks and ground reflections can interfere with target-feature extraction and increase detection errors. The authors identify region-of-interest narrowing, feature-dimension reduction, less frequent detection during low-risk periods and a lightweight rain-suppression module as areas for further work, but those steps were not evaluated in the reported experiments.

Subject of Research: Computer-vision detection and tracking of soccer offside positions in match video

Article Title: Automatic offside determination in soccer videos based on improved GMM-FFD-FDSST

Article References: Zhang, C., Dai, J., & Liu, Y. (2026). Automatic offside determination in soccer videos based on improved GMM-FFD-FDSST. Discover Artificial Intelligence, 6(1), Article 1199. https://doi.org/10.1007/s44163-026-02046-w

Image Credits: AI Generated

DOI: 10.1007/s44163-026-02046-w

Keywords: soccer offside, computer vision, video analysis, Gaussian mixture model, four-frame differencing, Canny edge detection, FDSST tracking, Kalman filtering, occlusion handling, sports analytics, Automatic, offside