# Spatio-activity based object detection

Source: [Spatio-activity based object detection](<https://www.sota2.com/research/paper/68fa5ff01e209131ce9ecc20>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-28T13:56:24.102Z

Authors: Jarrad Springett, Jeroen Vendrig

Published: 2008-03-11T13:40:42.000Z

## Abstract

We present the SAMMI lightweight object detection method which has a high level of accuracy and robustness, and which is able to operate in an environment with a large number of cameras. Background modeling is based on DCT coefficients provided by cameras. Foreground detection uses similarity in temporal characteristics of adjacent blocks of pixels, which is a computationally inexpensive way to make use of object coherence. Scene model updating uses the approximated median method for improved performance. Evaluation at pixel level and application level shows that SAMMI object detection performs better and faster than the conventional Mixture of Gaussians method.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/0803.1586>)

[PDF](<https://arxiv.org/pdf/0803.1586.pdf>)