Activity detection and counting people using Mask-RCNN with bidirectional ConvLSTM

Abstract

Image Incorporation concerns, including background confusion, uneven population distribution, and variations in scale and familiarity, can make group counting difficult. Pre-existing information and multi-level contextual representations are required to handle these problems effectively with deep neural networks and Mask-RCNN. Numerous studies on crowd counting use density maps without segmentation, which treat a group of individuals as a single entity. This article offers a hybrid method for crowd counting that combines Mask-RCNN (MRCNN) and a bidirectional convolutional long-term memory network (ConvLSTM), dubbed (CC: MRCNN-biCLSTM). The CC: MRCNN-biCLSTM is based on the Mask-RCN; it first segments instances and generates density maps, which are passed into adversarial learning during the training phase. Finally, the bidirectional convolutional LSTM is being used to return metrics and counts for individuals within a group of individuals. Following that, the suggested activity detection technique based on the Bayesian non-linear filter AD-BNF is used to identify a person’s activity. Additionally, the suggested approach resolves human grouping and enhances metric performance. Extensive studies demonstrate that the suggested method outperforms more sophisticated techniques on four frequently used difficult criteria for density map precision and quality.

Acknowledgment of patient in sense behaviors using bidirectionalConvLSTM

“…Our architecture expands the architecture described by interpreting image data in both directions using a Convolutional LSTM (ConvLSTM). 20 We hypothesize that accessing both previous and future information from an existing state enables the BiConvLSTM [21][22][23][24][25][26] to comprehend the meaning of the existing input, resulting in improved categorizing on non-homogenous and voluminous datasets. It demonstrates our networks' effectiveness in conducting experimentation on three standard datasets.…”

“…And finally, this interpretation is fed into a classifier that determines whether or not the image/video includes abuse. Our architecture expands the architecture described by interpreting image data in both directions using a Convolutional LSTM (ConvLSTM) 20 . We hypothesize that accessing both previous and future information from an existing state enables the BiConvLSTM 21–26 to comprehend the meaning of the existing input, resulting in improved categorizing on non‐homogenous and voluminous datasets.…”

Disaggregation of population estimates at high spatial resolution with machine learning

“…RNNs, LSTMs, gated recurrent units (GRUs) are designed to process sequential data, making them suitable for time series analysis, while combined with CNNs could improve the accuracy and granularity of local population projections. For example, convolutional long short-term memory networks (ConvLSTMs) have already been used to process time series of remote sensing images [81,63,131] and bear potential for applications on population projections.…”