My Account | Careers | Downloads | Blog

ABSTRACT:

We present a novel approach for visual detection and attribute-based search of vehicles in crowded surveillance scenes. Large-scale processing is addressed along two dimensions: 1) large-scale indexing, where hundreds of billions of events need to be archived per month to enable effective search and 2) learning vehicle detectors with large-scale feature selection, using a feature pool containing millions of feature descriptors. Our method for vehicle detection also explicitly models occlusions and multiple vehicle types (e.g., buses, trucks, SUVs, cars), while requiring very few manual labeling. It runs quite efficiently at an average of 66 Hz on a conventional laptop computer. Once a vehicle is detected and tracked over the video, fine-grained attributes are extracted and ingested into a database to allow future search queries such as “Show me all blue trucks larger than 7 ft. length traveling at high speed northbound last Saturday, from 2 pm to 5 pm”. We perform a comprehensive quantitative analysis to validate our approach, showing its usefulness in realistic urban surveillance settings.


Did you like this research project?

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

PROJECT TITLE : Parallel Fractional Hot-Deck Imputation and Variance Estimation for Big Incomplete Data Curing ABSTRACT: The fractional hot-deck imputation, also known as FHDI, is a method for handling multivariate missing data
PROJECT TITLE : Scalable and Practical Natural Gradient for Large-Scale Deep Learning ABSTRACT: Because of the increase in the effective mini-batch size, the generalization performance of the models produced by large-scale distributed
PROJECT TITLE : Large Scale Network Embedding A Separable Approach ABSTRACT: There have been many successful methods proposed for learning low-dimensional representations on large-scale networks; however, almost all of the methods
PROJECT TITLE : GAIN: Graph Attention & Interaction Network for Inductive Semi-Supervised Learning Over Large-Scale Graphs ABSTRACT: The state-of-the-art performance on a variety of machine learning tasks, including recommendation,
PROJECT TITLE : Cloud-Based Outsourcing for Enabling Privacy-Preserving Large-Scale Non-Negative Matrix Factorization ABSTRACT: It is inescapable and self-evident that clients with limited resources will find it necessary and

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

Project Enquiry