Silhouette-Based Method for Object Classification and Human Action Recognition in Video

Abstract

Abstract. In this paper we present an instance based machine learning algorithm and system for real-time object classification and human action recognition which can help to build intelligent surveillance systems. The proposed method makes use of object silhouettes to classify objects and actions of humans present in a scene monitored by a stationary camera. An adaptive background subtracttion model is used for object segmentation. Template matching based supervised learning method is adopted to classify objects into classes like human, human group and vehicle; and human actions into predefined classes like walking, boxing and kicking by making use of object silhouettes.

A Review on Video-Based Human Activity Recognition

“…Taking into account the distance information and the width feature of a silhouette, Lin et al [13] propose a new feature, called nonparametric weighted feature extraction (NWFE), to build histogram vectors for human activity recognition by using the nearest neighbor classifiers. NWFE features are extracted from the pose contour by combining the distance [102] and width features [103], which are then projected from the original high-dimensionality feature space to a low-dimensional subspace by PCA transformation and K-means clustering. The NWFE features reduce the computational complexity and still achieve high-recognition rate.…”

Image Description Using a Multiplier-Less Operator

“…Furthermore, many video processing applications require real-time solutions. In order to decrease the computational cost, we introduce the co-difference matrix as follows (2) where the operator acts like a matrix multiplication operator, however, the scalar multiplication is replaced by an additive operator . The operator is basically an addition operation but the sign of the result behaves like the multiplication operation:…”

“…Practical applications include intelligent video surveillance systems with object tracking, human and vehicle recognition and license plate recognition features [1], [2].…”

Flexible test-bed for unusual behavior detection

“…First, moving objects in video are segmented from the scene background by using an adaptive background subtraction algorithm and then segmented objects are classified into groups like human and human group using a silhouette based classification method [4]. By analyzing the motion of the human groups and at the same time detecting screams or increasing sound in audio a decision is given to detect fight.…”