Human-animal recognition in camera trap images

Simsek, Emrah; Ozyer, Baris; Bayındır, Levent; Özyer, Gülşah Tümüklü

doi:10.1109/siu.2018.8404700

Cited by 3 publications

(1 citation statement)

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“…Recently moving object detection in video sequences is required for making analysis more intelligent aspect in computer vision area such as surveillance in daily and wild life, retrieval and recognition [1][2][3][4][5][6][7][8]. The key issue of moving object in image frame can be defined as a region collected by a set of connected pixel that are segmented from the background.…”

Section: Introductionmentioning

confidence: 99%

Selected Three Frame Difference Method for Moving Object Detection

Simsek¹,

Ozyer²

2021

ijisae

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Three frame difference is one of the well-known methods that is used to perform moving object detection. According to the theory, the presence of a moving object is estimated by subtracting consecutive three image frames that provide moving object edges. However, these edges do not give complete information of the moving object which means that the method leads to loss of information. Some post-processing methods such as morphological operations, optical flow and combining these techniques are necessary to be apply for obtaining complete information of moving object. In this paper, we present a new approach called Selected Three Frame Difference (STFD) to detect moving object in video sequences without any post processing operations. We initially propose an algorithm that selects three images considering the local maximum value of frame differences. Instead of using consecutive three frames, these three selected image differences that include non-overlapping object frames are applied to the logical and operator. We mathematically prove that the entire moving object is always detected in the second selected image. We analyzed the proposed method on public benchmark dataset and the dataset collected from our laboratory. To validate the performance of our approach, we also compared with three frame difference method and background subtraction based traditional moving object detection methods on a few sample videos selected from different datasets.

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