2 research outputs found
CNN-Based Prediction of Frame-Level Shot Importance for Video Summarization
In the Internet, ubiquitous presence of redundant, unedited, raw videos has
made video summarization an important problem. Traditional methods of video
summarization employ a heuristic set of hand-crafted features, which in many
cases fail to capture subtle abstraction of a scene. This paper presents a deep
learning method that maps the context of a video to the importance of a scene
similar to that is perceived by humans. In particular, a convolutional neural
network (CNN)-based architecture is proposed to mimic the frame-level shot
importance for user-oriented video summarization. The weights and biases of the
CNN are trained extensively through off-line processing, so that it can provide
the importance of a frame of an unseen video almost instantaneously.
Experiments on estimating the shot importance is carried out using the publicly
available database TVSum50. It is shown that the performance of the proposed
network is substantially better than that of commonly referred feature-based
methods for estimating the shot importance in terms of mean absolute error,
absolute error variance, and relative F-measure.Comment: Accepted in International Conference on new Trends in Computer
Sciences (ICTCS), Amman-Jordan, 201
Hyper RPCA: Joint Maximum Correntropy Criterion and Laplacian Scale Mixture Modeling On-the-Fly for Moving Object Detection
Moving object detection is critical for automated video analysis in many
vision-related tasks, such as surveillance tracking, video compression coding,
etc. Robust Principal Component Analysis (RPCA), as one of the most popular
moving object modelling methods, aims to separate the temporally varying (i.e.,
moving) foreground objects from the static background in video, assuming the
background frames to be low-rank while the foreground to be spatially sparse.
Classic RPCA imposes sparsity of the foreground component using l1-norm, and
minimizes the modeling error via 2-norm. We show that such assumptions can be
too restrictive in practice, which limits the effectiveness of the classic
RPCA, especially when processing videos with dynamic background, camera jitter,
camouflaged moving object, etc. In this paper, we propose a novel RPCA-based
model, called Hyper RPCA, to detect moving objects on the fly. Different from
classic RPCA, the proposed Hyper RPCA jointly applies the maximum correntropy
criterion (MCC) for the modeling error, and Laplacian scale mixture (LSM) model
for foreground objects. Extensive experiments have been conducted, and the
results demonstrate that the proposed Hyper RPCA has competitive performance
for foreground detection to the state-of-the-art algorithms on several
well-known benchmark datasets