Search Results for author:
Found 13 papers, 4 papers with code
Latent Bernoulli Autoencoder
In this work, we pose a question whether it is possible to design and train an autoencoder model in an end-to-end fashion to learn latent representations in multivariate Bernoulli space, and achieve performance comparable with the current state-of-the-art variational methods.
Content-aware Density Map for Crowd Counting and Density Estimation
Precise knowledge about the size of a crowd, its density and flow can provide valuable information for safety and security applications, event planning, architectural design and to analyze consumer behavior.
Smart IoT Cameras for Crowd Analysis based on augmentation for automatic pedestrian detection, simulation and annotation
In order to provide related behavioural models, simulation and tracking approaches have been considered in the literature.
Scene and Environment Monitoring Using Aerial Imagery and Deep Learning
In this paper, a review of studies applying deep learning on UAV imagery for smart farming is presented.
Semi-supervised GAN for Classification of Multispectral Imagery Acquired by UAVs
The results by the proposed semi-supervised GAN achieves high classification accuracy and demonstrates the potential of GAN-based methods for the challenging task of multispectral image classification.
A Comparison of Embedded Deep Learning Methods for Person Detection
Recent advancements in parallel computing, GPU technology and deep learning provide a new platform for complex image processing tasks such as person detection to flourish.
Summarizing Videos with Attention
In this work we propose a novel method for supervised, keyshots based video summarization by applying a conceptually simple and computationally efficient soft, self-attention mechanism.
Ranked #3 on
Video Summarization
on TvSum
(using extra training data)
Object 3D Reconstruction based on Photometric Stereo and Inverted Rendering
Our suggested approach is to recover scene properties in the presence of indirect illumination.
Superframes, A Temporal Video Segmentation
The goal of video segmentation is to turn video data into a set of concrete motion clusters that can be easily interpreted as building blocks of the video.
AMNet: Memorability Estimation with Attention
Further on we study the impact of the attention mechanism on the memorability estimation and evaluate our network on the SUN Memorability and the LaMem datasets.
Deep-Plant: Plant Identification with convolutional neural networks
This paper studies convolutional neural networks (CNN) to learn unsupervised feature representations for 44 different plant species, collected at the Royal Botanic Gardens, Kew, England.
Detection of Salient Regions in Crowded Scenes
The increasing number of cameras and a handful of human operators to monitor the video inputs from hundreds of cameras leave the system ill equipped to fulfil the task of detecting anomalies.
Refined Particle Swarm Intelligence Method for Abrupt Motion Tracking
Conventional tracking solutions are not feasible in handling abrupt motion as they are based on smooth motion assumption or an accurate motion model.
