From 933d7e0f7c2fb86c94a595f8c5a368a71ba0723f Mon Sep 17 00:00:00 2001 From: Chiara Santoro Date: Sun, 26 Apr 2026 17:50:56 +0200 Subject: [PATCH] v5 group_o.html --- index.html | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/index.html b/index.html index 10bb48a..0574491 100644 --- a/index.html +++ b/index.html @@ -259,12 +259,13 @@

Facial Expression Recognition with Hybrid Models

Smart Event Detection for Highlight Clips

Have you ever missed a highlight during a match? This system can capture highlights based on a user prompt or directly from a video. - It uses advanced, state-of-the-art approaches—such as Meta’s SAM3, to track objects, detect events, and generate short highlight clips. + It uses advanced, state-of-the-art approaches, such as Meta’s SAM3, to track objects, detect events, and generate short highlight clips.

The goal is to combine modern segmentation models (such as SAM) with classical computer vision techniques. Segmentation serves as a strong perception layer, while event detection is driven by motion-based features such as trajectories, velocity, and frequency analysis, along with lightweight reasoning. The system follows a modular design, consisting of a general perception and feature extraction pipeline combined with task-specific event detection modules.

The system is primarily designed for human action detection (e.g., waving, raising a hand, standing up). As an extension, it can also handle simple sports scenarios, such as tracking a ball moving toward or crossing a goal, demonstrating its ability to generalize to multi-object interactions. +