7,115 research outputs found
Action recognition based on efficient deep feature learning in the spatio-temporal domain
© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Hand-crafted feature functions are usually designed based on the domain knowledge of a presumably controlled environment and often fail to generalize, as the statistics of real-world data cannot always be modeled correctly. Data-driven feature learning methods, on the other hand, have emerged as an alternative that often generalize better in uncontrolled environments. We present a simple, yet robust, 2D convolutional neural network extended to a concatenated 3D network that learns to extract features from the spatio-temporal domain of raw video data. The resulting network model is used for content-based recognition of videos. Relying on a 2D convolutional neural network allows us to exploit a pretrained network as a descriptor that yielded the best results on the largest and challenging ILSVRC-2014 dataset. Experimental results on commonly used benchmarking video datasets demonstrate that our results are state-of-the-art in terms of accuracy and computational time without requiring any preprocessing (e.g., optic flow) or a priori knowledge on data capture (e.g., camera motion estimation), which makes it more general and flexible than other approaches. Our implementation is made available.Peer ReviewedPostprint (author's final draft
Improving a 3-D Convolutional Neural Network Model Reinvented from VGG16 with Batch Normalization
It is challenging to build and train a Convolutional Neural Network model that can achieve a high accuracy rate for the first time. There are many variables to consider such as initial parameters, learning rate, and batch size. Unsuccessfully training a model is one of the most inevitable problems. In some cases, the model struggles to find a lower Loss Function value which results in a poor performance. Batch Normalization is considered as a remedy to overcome this problem. In this paper, two models reinvented from VGG16 are created with and without using Batch Normalization to evaluate their model performance. It is clear that the model using Batch Normalization provides a better result in terms of Loss Function value and model accuracy, which also achieves a very high accuracy rate. It also reaches the saturation point of the highest model accuracy faster than the model without Batch Normalization. This paper also finds that the accuracy of 3D Convolutional Neural Network model reinvented from VGG16 with Batch Normalization is at 91.2% which can beat many benchmarking results on UCF101 such as IDT [5], Two-Stream [10], and Dynamic Image Networks IDT [4]. The technique introduced in this paper shows a fast, reliable and accurate estimation of human activity type and could be used in smart environments
Activity Recognition based on a Magnitude-Orientation Stream Network
The temporal component of videos provides an important clue for activity
recognition, as a number of activities can be reliably recognized based on the
motion information. In view of that, this work proposes a novel temporal stream
for two-stream convolutional networks based on images computed from the optical
flow magnitude and orientation, named Magnitude-Orientation Stream (MOS), to
learn the motion in a better and richer manner. Our method applies simple
nonlinear transformations on the vertical and horizontal components of the
optical flow to generate input images for the temporal stream. Experimental
results, carried on two well-known datasets (HMDB51 and UCF101), demonstrate
that using our proposed temporal stream as input to existing neural network
architectures can improve their performance for activity recognition. Results
demonstrate that our temporal stream provides complementary information able to
improve the classical two-stream methods, indicating the suitability of our
approach to be used as a temporal video representation.Comment: 8 pages, SIBGRAPI 201
Learning Spatiotemporal Features for Infrared Action Recognition with 3D Convolutional Neural Networks
Infrared (IR) imaging has the potential to enable more robust action
recognition systems compared to visible spectrum cameras due to lower
sensitivity to lighting conditions and appearance variability. While the action
recognition task on videos collected from visible spectrum imaging has received
much attention, action recognition in IR videos is significantly less explored.
Our objective is to exploit imaging data in this modality for the action
recognition task. In this work, we propose a novel two-stream 3D convolutional
neural network (CNN) architecture by introducing the discriminative code layer
and the corresponding discriminative code loss function. The proposed network
processes IR image and the IR-based optical flow field sequences. We pretrain
the 3D CNN model on the visible spectrum Sports-1M action dataset and finetune
it on the Infrared Action Recognition (InfAR) dataset. To our best knowledge,
this is the first application of the 3D CNN to action recognition in the IR
domain. We conduct an elaborate analysis of different fusion schemes (weighted
average, single and double-layer neural nets) applied to different 3D CNN
outputs. Experimental results demonstrate that our approach can achieve
state-of-the-art average precision (AP) performances on the InfAR dataset: (1)
the proposed two-stream 3D CNN achieves the best reported 77.5% AP, and (2) our
3D CNN model applied to the optical flow fields achieves the best reported
single stream 75.42% AP
Multi-View Region Adaptive Multi-temporal DMM and RGB Action Recognition
Human action recognition remains an important yet challenging task. This work
proposes a novel action recognition system. It uses a novel Multiple View
Region Adaptive Multi-resolution in time Depth Motion Map (MV-RAMDMM)
formulation combined with appearance information. Multiple stream 3D
Convolutional Neural Networks (CNNs) are trained on the different views and
time resolutions of the region adaptive Depth Motion Maps. Multiple views are
synthesised to enhance the view invariance. The region adaptive weights, based
on localised motion, accentuate and differentiate parts of actions possessing
faster motion. Dedicated 3D CNN streams for multi-time resolution appearance
information (RGB) are also included. These help to identify and differentiate
between small object interactions. A pre-trained 3D-CNN is used here with
fine-tuning for each stream along with multiple class Support Vector Machines
(SVM)s. Average score fusion is used on the output. The developed approach is
capable of recognising both human action and human-object interaction. Three
public domain datasets including: MSR 3D Action,Northwestern UCLA multi-view
actions and MSR 3D daily activity are used to evaluate the proposed solution.
The experimental results demonstrate the robustness of this approach compared
with state-of-the-art algorithms.Comment: 14 pages, 6 figures, 13 tables. Submitte
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