2,035 research outputs found
Learning to Extract Motion from Videos in Convolutional Neural Networks
This paper shows how to extract dense optical flow from videos with a
convolutional neural network (CNN). The proposed model constitutes a potential
building block for deeper architectures to allow using motion without resorting
to an external algorithm, \eg for recognition in videos. We derive our network
architecture from signal processing principles to provide desired invariances
to image contrast, phase and texture. We constrain weights within the network
to enforce strict rotation invariance and substantially reduce the number of
parameters to learn. We demonstrate end-to-end training on only 8 sequences of
the Middlebury dataset, orders of magnitude less than competing CNN-based
motion estimation methods, and obtain comparable performance to classical
methods on the Middlebury benchmark. Importantly, our method outputs a
distributed representation of motion that allows representing multiple,
transparent motions, and dynamic textures. Our contributions on network design
and rotation invariance offer insights nonspecific to motion estimation
Flow-Guided Feature Aggregation for Video Object Detection
Extending state-of-the-art object detectors from image to video is
challenging. The accuracy of detection suffers from degenerated object
appearances in videos, e.g., motion blur, video defocus, rare poses, etc.
Existing work attempts to exploit temporal information on box level, but such
methods are not trained end-to-end. We present flow-guided feature aggregation,
an accurate and end-to-end learning framework for video object detection. It
leverages temporal coherence on feature level instead. It improves the
per-frame features by aggregation of nearby features along the motion paths,
and thus improves the video recognition accuracy. Our method significantly
improves upon strong single-frame baselines in ImageNet VID, especially for
more challenging fast moving objects. Our framework is principled, and on par
with the best engineered systems winning the ImageNet VID challenges 2016,
without additional bells-and-whistles. The proposed method, together with Deep
Feature Flow, powered the winning entry of ImageNet VID challenges 2017. The
code is available at
https://github.com/msracver/Flow-Guided-Feature-Aggregation
Deep Learning for Crowd Anomaly Detection
Today, public areas across the globe are monitored by an increasing amount of surveillance cameras. This widespread usage has presented an ever-growing volume of data that cannot realistically be examined in real-time. Therefore, efforts to understand crowd dynamics have brought light to automatic systems for the detection of anomalies in crowds. This thesis explores the methods used across literature for this purpose, with a focus on those fusing dense optical flow in a feature extraction stage to the crowd anomaly detection problem. To this extent, five different deep learning architectures are trained using optical flow maps estimated by three deep learning-based techniques. More specifically, a 2D convolutional network, a 3D convolutional network, and LSTM-based convolutional recurrent network, a pre-trained variant of the latter, and a ConvLSTM-based autoencoder is trained using both regular frames and optical flow maps estimated by LiteFlowNet3, RAFT, and GMA on the UCSD Pedestrian 1 dataset. The experimental results have shown that while prone to overfitting, the use of optical flow maps may improve the performance of supervised spatio-temporal architectures
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