Human-Pose-Detection

Objective

According to NCRB, 2.97 million cases of crime recorded in year 2018. The project propose a solution for remote monitoring and analysis, suitable an aerial vehicle - Suspicious activity detection through video analysis, primarily for human pose detection using visual features.

Project information

Every activity has a particular pose associated with it.
Total 4 activities are consider for the scope of this work:
1. Slap
2. Kick
3. Shoot
4. Normal
A comparative analysis of existing classifier to suite the data set.

Code Execution Instructions

Command to download large files

git lfs fetch --all

Requirements

Python (ver >= 3.4)
Numpy
Sklearn
OpenCv

Steps

Orientation Extraction on Images

python OpenPoseImage.py

Training (Results stored in 'orient_train.csv')

python multi-person-train.py

Classification on video (sample video: 'etc/d_fight.mp4') - Using Dtree/ KNN classifiers

python multi-person-classify_video_dtree.py -v video_path

python multi-person-classify_video_knn.py -v video_path

Testing (Results stored in 'orient_test_result.csv')

python multi-person-classify_test_knn.py

Process Flow

Pose Estimation

Z. Cao has proposed mutli-person pose estimation with using CNN
Two branches - One for body part location and other for affinity between them.

Orientation Extraction

Angle with 13 major pairs of body is considered such as Shoulder to Elbow and so on.
Angles are inverted and w.r.t to horizontal axis.

Classification

Simple classification algorithms such as KNN, Decision Tree and Naive Bayes can be trained and used for classification.

Analysis of Classification Techniques

Performance of classifier of great importance
Cross-validation is used for better evaluation of classifiers.

Results

Output of the proposed method

Pre-processing of Data

Due to obstruction in the scenario many body parts will not get covered.
A weak assumption that those body parts are vertically straight is made.(highlighted by yellow color)