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We are considering further integration with a body part detection method to improve robustness to occlusion.
It presents and evaluates the Region Comparison (RC) features for fast and accurate body part detection.
To increase the robustness of part detection, the semantic parts are represented by their detection score maps.
The two-stage search loop stops before the angular range of the searched edge is greater than 240° or will turn to the longest skyline part detection.
Unlike other multi-layer models, our approach explores more reasonable granularity for part detection and sophisticatedly designs part connections to model body configurations more effectively.
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Further, the technique will use a two-part detection scheme to ensure that point anomalies are detected in real-time and then evaluated using contextual clustering.
This is useful information because open-source facial recognition is relatively advanced while other-body-part detection is much less so.
This method consists of two parts: detection and filtering.
The transformation is divided into two parts: detection of cycles and iterative resolution of these cycles.
N. Jaisankar et al. propose a security approach which is composed of two parts, detection and reaction.
A number of related research problems, such as feature representations, human pose and body parts detection, and scene/object context, are being actively studied.
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CEO of Professional Science Editing for Scientists @ prosciediting.com