scispace - formally typeset
Book ChapterDOI

Moving Object Detection: A New Approach

Reads0
Chats0
TLDR
This chapter presents a novel background subtraction technique for detecting moving objects from video under dynamic background conditions that would be useful particularly for applications in resource constrained environments by virtue of its low computation time and storage requirements.
Abstract
This chapter presents a novel background subtraction technique for detecting moving objects from video under dynamic background conditions. The presented methodology is a simple and low-cost solution for modeling and updating background during a background subtraction process. Comparative performance analysis with other state-of-the-art methods on benchmark data-set shows the effectiveness of the proposed method. The objective of the research is not to claim that the proposed method yields the best result in terms of accuracy; rather the main novelty is in its low computational cost. However, in spite of being less computation-intensive, comparative analysis reveals that the new method produces quite competitive results as compared to other methods. The proposed method would be useful particularly for applications in resource constrained environments by virtue of its low computation time and storage requirements.

read more

Citations
More filters
Journal ArticleDOI

Review of Human Motion Detection based on Background Subtraction Techniques

TL;DR: This paper provides a review of the human motion detection methods focusing on background subtraction technique and concludes that current methods for detecting objects in motion within videos from static cameras are inadequate.
Journal ArticleDOI

Automatic Dynamic Range Adjustment for Pedestrian Detection in Thermal (Infrared) Surveillance Videos

TL;DR: The proposed method uses a combination of histogram specification and iterative histogram partitioning to progressively adjust the dynamic range and efficiently suppress the background of each video frame to ensure that pedestrians are present in the image at the convergence point of the algorithm.
Journal ArticleDOI

Spatio-Temporal Processing for Automatic Vehicle Detection in Wide-Area Aerial Video

TL;DR: A spatio-temporal processing scheme to improve automatic vehicle detection performance by replacing the thresholding step of existing detection algorithms with multi-neighborhood hysteresis thresholding for foreground pixel classification is presented.
Book ChapterDOI

A novel approach for human silhouette extraction from video data

TL;DR: In this paper, the authors proposed a method for efficient extraction of human silhouette from video sequences using background elimination, edge detection, region filling and noise removal using morphological operations to estimate the silhouette of an image.
References
More filters
Proceedings ArticleDOI

Adaptive background mixture models for real-time tracking

TL;DR: This paper discusses modeling each pixel as a mixture of Gaussians and using an on-line approximation to update the model, resulting in a stable, real-time outdoor tracker which reliably deals with lighting changes, repetitive motions from clutter, and long-term scene changes.
Journal ArticleDOI

Learning patterns of activity using real-time tracking

TL;DR: This paper focuses on motion tracking and shows how one can use observed motion to learn patterns of activity in a site and create a hierarchical binary-tree classification of the representations within a sequence.
Proceedings ArticleDOI

Background subtraction techniques: a review

TL;DR: A review of the main methods and an original categorisation based on speed, memory requirements and accuracy can effectively guide the designer to select the most suitable method for a given application in a principled way.
Journal ArticleDOI

Detecting moving objects, ghosts, and shadows in video streams

TL;DR: A general-purpose method is proposed that combines statistical assumptions with the object-level knowledge of moving objects, apparent objects (ghosts), and shadows acquired in the processing of the previous frames to improve object segmentation and background update.
Proceedings ArticleDOI

Changedetection.net: A new change detection benchmark dataset

TL;DR: A unique change detection benchmark dataset consisting of nearly 90,000 frames in 31 video sequences representing 6 categories selected to cover a wide range of challenges in 2 modalities (color and thermal IR).
Related Papers (5)