An application which is self-sufficient to describe normal activities like Walking, Waving, Running, Jumping, etc. of Human as an object in a live or recorded video.

we are a team of persons sharing a lot of knowledge in artificial intelligence field. we are experts at machine learning, deep learning, artificial intelligence and computer vision, we work on many environments and different libraries such as NumPy, TensorFlow, pandas, SKlearn, openCV, graphlab and matplotlib. we are experts in applying machine learning algorithms and optimizing it such as regression, classification, clustering, reinforcement learning and deep learning. we are experts in python, C, C++ and C# . we are experts in visualizing data to be seen by the client in a simple way.

I have fulfilled a lot of such requirements earlier. I am capable of implementing a cnn from scratch, using a tensorflow pretrained model, and custom training a model using tensorflow model configuration file. Therefore, I find myself suitable for your task. Moreover we have datasets of jogging and walking available from models like yolo and coco. Hoping to hear from you soon.

Hi! I take this is a Spatio-temporal problem which is of course not easy, as we need to link/stack frames in videos for better classification. I can definitely help! I may be new to freelancer but not my experience (have a publication in AI/DL :)

I have experience in implementing ML and DL models using tenserflow, keras and scikit-learn. I have completed many projects. Some of these are as: 1. Human Activity Recognition (HAR) :- I have tried three models Support Vector Machines (SVM), K-Nearest Neighbors (k-NN) and Convolutional Neural Network (CNN) to classify six activities(Walking ,Walking Upstairs, Walking Downstairs, Sitting, Standing, Laying). The most accurate model tried is SVM with accuracy of 98.47%. 2. Brain Computer Interface(BCI) with the P300 Speller which aims for helping patients incapable to activate muscles to spell words by methods for their brain signal activities. The first classification is to identify the presence of a P300 in the electroencephalogram(EEG). The second classification is the combination of various P300 response for deciding the correct character to spell. 3. Handwritten Digit Recognition using CNN :- Implemented a CNN model using tenserflow to identify handwritten digits from the MNIST dataset. I will first discuss problem statement and your requirement properly and then I will propose an approach and estimated outcomes with you and if you will agree with that then I will code that and complete it. Your requirement and statisfaction will be my first priority. Looking forward to hearing from you. Thank You.

I am computer vision developer having prior experience in such works, so here is my approach:- i will train YOLO v2 model on the dataset of running, walking, jumping etc. as per requirement of the job post, it will surely give good result in detecting the human activities like running, walking, jumping etc. and will display the predicted output on the frame.