Image enhancement by the combination of multivalued logic and Savitzky–Golay filter

Karmakar, Sayantani; Karmakar, Supriya

doi:10.1080/21681724.2018.1494317

Cited by 2 publications

(1 citation statement)

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“…For the rest, we fill small gaps with the closest previous or future value. We apply the Savitsky-Golay filter [38] to exclude random spikes and get smooth time series for the movement. is filter increased the precision without altering any interest points and as a result, we have the vector of 68 gradients of interest points with the total number of frames in the video.…”

Section: Mathematical Problems In Engineeringmentioning

confidence: 99%

Robust Suspicious Action Recognition Approach Using Pose Descriptor

Ahmed

Yousaf

Yasin

2021

Mathematical Problems in Engineering

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In the current era of technological development, human actions can be recorded in public places like airports, shopping malls, and educational institutes, etc., to monitor suspicious activities like terrorism, fighting, theft, and vandalism. Surveillance videos contain adequate visual and motion information for events that occur within a camera’s view. Our study focuses on the concept that actions are a sequence of moving body parts. In this paper, a new descriptor is proposed that formulates human poses and tracks the relative motion of human body parts along with the video frames, and extracts the position and orientation of body parts. We used Part Affinity Fields (PAFs) to acquire the associated body parts of the people present in the frame. The architecture jointly learns the body parts and their associations with other body parts in a sequential process, such that a pose can be formulated step by step. We can obtain the complete pose with a limited number of points as it moves along the video and we can conclude with a defined action. Later, these feature points are classified with a Support Vector Machine (SVM). The proposed work was evaluated on the benchmark datasets, namely, UT-interaction, UCF11, CASIA, and HCA datasets. Our proposed scheme was evaluated on the aforementioned datasets, which contained criminal/suspicious actions, such as kick, punch, push, gun shooting, and sword-fighting, and achieved an accuracy of 96.4% on UT-interaction, 99% on UCF11, 98% on CASIA and 88.72% on HCA.

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Section: Mathematical Problems In Engineeringmentioning

confidence: 99%