Towards Collaborative Robotics in Top View Surveillance:A Framework for Multiple Object Tracking by Detection Using Deep Learning

Collaborative Robotics is one of the high-interest research topics in the area of academia and industry.It has been progressively utilized in numerous applications,particularly in intelligent surveillance systems.It allows the deployment of smart cameras or optical sensors with computer vision techniques,which may serve in several object detection and tracking tasks.These tasks have been considered challenging and high-level perceptual problems,frequently dominated by relative information about the environment,where main concerns such as occlusion,illumination,background,object deformation,and object class variations are commonplace.In order to show the importance of top view surveillance,a collaborative robotics framework has been presented.It can assist in the detection and tracking of multiple objects in top view surveillance.The framework consists of a smart robotic camera embedded with the visual processing unit.The existing pre-trained deep learning models named SSD and YOLO has been adopted for object detection and localization.The detection models are further combined with different tracking algorithms,including GOTURN,MEDIANFLOW,TLD,KCF,MIL,and BOOSTING.These algorithms,along with detection models,help to track and predict the trajectories of detected objects.The pre-trained models are employed;therefore,the generalization performance is also investigated through testing the models on various sequences of top view data set.The detection models achieved maximum True Detection Rate 93%to 90%with a maximum 0.6%False Detection Rate.The tracking results of different algorithms are nearly identical,with tracking accuracy ranging from 90%to 94%.Furthermore,a discussion has been carried out on output results along with future guidelines.

Automated Patient Discomfort Detection Using Deep Learning

The Internet of Things(IoT)has been transformed almost all fields of life,but its impact on the healthcare sector has been notable.Various IoTbased sensors are used in the healthcare sector and offer quality and safe care to patients.This work presents a deep learning-based automated patient discomfort detection system in which patients’discomfort is non-invasively detected.To do this,the overhead view patients’data set has been recorded.For testing and evaluation purposes,we investigate the power of deep learning by choosing a Convolution Neural Network(CNN)based model.The model uses confidence maps and detects 18 different key points at various locations of the body of the patient.Applying association rules and part affinity fields,the detected key points are later converted into six main body organs.Furthermore,the distance of subsequent key points is measured using coordinates information.Finally,distance and the time-based threshold are used for the classification of movements associated with discomfort or normal conditions.The accuracy of the proposed system is assessed on various test sequences.The experimental outcomes reveal the worth of the proposed system’by obtaining a True Positive Rate of 98%with a 2%False Positive Rate.

Convolutional Neural Network for Histopathological Osteosarcoma Image Classification

Osteosarcoma is one of the most widespread causes of bone cancer globally and has a high mortality rate.Early diagnosis may increase the chances of treatment and survival however the process is time-consuming(reliability and complexity involved to extract the hand-crafted features)and largely depends on pathologists’experience.Convolutional Neural Network(CNN—an end-to-end model)is known to be an alternative to overcome the aforesaid problems.Therefore,this work proposes a compact CNN architecture that has been rigorously explored on a Small Osteosarcoma histology Image Dataaseet(a high-class imbalanced dataset).Though,during training,class-imbalanced data can negatively affect the performance of CNN.Therefore,an oversampling technique has been proposed to overcome the aforesaid issue and improve generalization performance.In this process,a hierarchical CNN model is designed,in which the former model is non-regularized(due to dense architecture)and the later one is regularized,specifically designed for small histopathology images.Moreover,the regularized model is integrated with CNN’s basic architecture to reduce overfitting.Experimental results demonstrate that oversampling might be an effective way to address the imbalanced class problem during training.The training and testing accuracies of the non-regularized CNN model are 98%&78%with an imbalanced dataset and 96%&81%with a balanced dataset,respectively.The regularized CNN model training and testing accuracies are 84%&75%for an imbalanced dataset and 87%&86%for a balanced dataset.