2 research outputs found
Detection, Recognition and Tracking of Moving Objects from Real-time Video via SP Theory of Intelligence and Species Inspired PSO
In this paper, we address the basic problem of recognizing moving objects in
video images using SP Theory of Intelligence. The concept of SP Theory of
Intelligence which is a framework of artificial intelligence, was first
introduced by Gerard J Wolff, where S stands for Simplicity and P stands for
Power. Using the concept of multiple alignment, we detect and recognize object
of our interest in video frames with multilevel hierarchical parts and
subparts, based on polythetic categories. We track the recognized objects using
the species based Particle Swarm Optimization (PSO). First, we extract the
multiple alignment of our object of interest from training images. In order to
recognize accurately and handle occlusion, we use the polythetic concepts on
raw data line to omit the redundant noise via searching for best alignment
representing the features from the extracted alignments. We recognize the
domain of interest from the video scenes in form of wide variety of multiple
alignments to handle scene variability. Unsupervised learning is done in the SP
model following the DONSVIC principle and natural structures are discovered via
information compression and pattern analysis. After successful recognition of
objects, we use species based PSO algorithm as the alignments of our object of
interest is analogues to observation likelihood and fitness ability of species.
Subsequently, we analyze the competition and repulsion among species with
annealed Gaussian based PSO. We have tested our algorithms on David, Walking2,
FaceOcc1, Jogging and Dudek, obtaining very satisfactory and competitive
results
A Proposed Artificial intelligence Model for Real-Time Human Action Localization and Tracking
In recent years, artificial intelligence (AI) based on deep learning (DL) has
sparked tremendous global interest. DL is widely used today and has expanded
into various interesting areas. It is becoming more popular in cross-subject
research, such as studies of smart city systems, which combine computer science
with engineering applications. Human action detection is one of these areas.
Human action detection is an interesting challenge due to its stringent
requirements in terms of computing speed and accuracy. High-accuracy real-time
object tracking is also considered a significant challenge. This paper
integrates the YOLO detection network, which is considered a state-of-the-art
tool for real-time object detection, with motion vectors and the Coyote
Optimization Algorithm (COA) to construct a real-time human action localization
and tracking system. The proposed system starts with the extraction of motion
information from a compressed video stream and the extraction of appearance
information from RGB frames using an object detector. Then, a fusion step
between the two streams is performed, and the results are fed into the proposed
action tracking model. The COA is used in object tracking due to its accuracy
and fast convergence. The basic foundation of the proposed model is the
utilization of motion vectors, which already exist in a compressed video bit
stream and provide sufficient information to improve the localization of the
target action without requiring high consumption of computational resources
compared with other popular methods of extracting motion information, such as
optical flows. This advantage allows the proposed approach to be implemented in
challenging environments where the computational resources are limited, such as
Internet of Things (IoT) systems.Comment: SUBMITTED TO IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING
SYSTEM