PROJECT TITLE :

From Handcrafted to Deep Features for Pedestrian Detection A Survey

ABSTRACT:

Detecting pedestrians is an important but difficult problem in the field of computer vision, particularly in activities that are focused on people. With the assistance of handcrafted features and deep features, a considerable amount of progress has been seen over the course of the past decade. The following is an exhaustive review of recent developments in pedestrian detection that have taken place. In the first step of this process, we offer a comprehensive analysis of single-spectral pedestrian detection, which covers both handcrafted features-based methods and deep features-based approaches. When it comes to methods that are based on handcrafted features, we present a comprehensive review of different approaches and discover that handcrafted features that have a large number of degrees of freedom in both shape and space have superior performance. In the case of approaches that are based on deep features, we divided them into two categories: those that only use CNN-based features and those that use features that are handcrafted in addition to CNN-based features. We provide a statistical analysis and trend of these methods, noting that feature-enhanced, part-aware, and post-processing methods have garnered the majority of the attention. In this article, we not only discuss the detection of pedestrians using a single spectral band, but also multi-spectral pedestrian detection, which offers more robust features for varying levels of illumination. In addition to this, we present a comprehensive experimental analysis as well as some relevant datasets and evaluation metrics. As a way to wrap up this survey, we will focus on open issues that still need to be resolved and will highlight the many different possible future paths.


Did you like this research project?

To get this research project Guidelines, Training and Code... Click Here


PROJECT TITLE : Revenue-Optimal Auction For Resource Allocation in Wireless Virtualization: A Deep Learning Approach ABSTRACT: Virtualization of wireless networks has emerged as an essential component of future cellular networks.
PROJECT TITLE : CNN-LSTM: Hybrid Deep Neural Network for Network Intrusion Detection System ABSTRACT: The importance of network security to our day-to-day interactions and networks cannot be overstated. The importance of having
PROJECT TITLE : Traffic Anomaly Detection in Wireless Sensor Networks Based on Principal Component Analysis and Deep Convolution Neural Network ABSTRACT: Because of the proliferation of wireless networks, wireless sensor networks
PROJECT TITLE : Traffic Signal Control Using End-to-End Off-Policy Deep Reinforcement Learning ABSTRACT: However, road intersections have historically been among the most significant traffic bottlenecks that have contributed
PROJECT TITLE : Spatio-Contextual Deep Network Based Multimodal Pedestrian Detection For Autonomous Driving ABSTRACT: The most important component of an autonomous driving system is the module that is responsible for pedestrian

Ready to Complete Your Academic MTech Project Work In Affordable Price ?

Project Enquiry