We investigate architectures of discriminatively trained deep Convolutional Net- works (ConvNets) for action recognition in video. The challenge is to capture the complementary information on appearance from still frames and motion be- tween frames. We also aim to generalise the best performing hand-crafted features within a data-driven learning framework.
Our contribution is three-fold. First, we propose a two-stream ConvNet architec- ture which incorporates spatial and temporal networks. Second, we demonstrate that a ConvNet trained on multi-frame dense optical flow is able to achieve very good performance in spite of limited training data. Finally, we show that multi- task learning, applied to two different action classification
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The spatial stream performs action recognition from still video frames, whilst the temporal stream is trained to recognise action from motion in the form of dense optical flow. Both streams are implemented as ConvNets. Decoupling the spatial and temporal nets also allows us to exploit the availability of large amounts of annotated image data by pre-training the spatial net on the ImageNet challenge dataset [1]. Our proposed architecture is related to the two-streams hypothesis [9], according to which the human visual cortex contains two pathways: the ventral stream (which performs object recognition) and the dorsal stream (which recognises motion); though we do not investigate this connection any further here.
The rest of the paper is organised as follows. In Sect. 1.1 we review the related work on action recognition using both shallow and deep architectures. In Sect. 2 we introduce the two-stream architecture and specify the Spatial ConvNet. Sect. 3 introduces the Temporal ConvNet and in particular how it generalizes the previous architectures reviewed in Sect. 1.1. A mult-task learning framework is developed in Sect. 4 in order to allow effortless combination of training data over 1 arXiv:1406.2199v2 [cs.CV] 12 Nov 2014 multiple datasets. Implementation details are given in Sect. 5, and the performance is evaluated in Sect. 6 and compared to the state of the art. Our experiments on two challenging datasets (UCF- 101 [24] and HMDB-51 [16])
• Feature Extraction : this is the most important stage for automated markerless capture systems whether for gait recognition, activity classification or other
For activity 2, the frame is run through a thresholding function that creates a binary image based on the histogram levels that are similar to that of a stop sign. Any pixel that falls within the levels are then converted to a value of one and the rest are set to zero. The binary image is then put through two morphological operators, open and close. The open morphological operator gets rid of smaller clutters of pixels as defined by the parameters that are passed in to the function. The close morphological operator fills in clusters of pixels that are the shape of the parameter passed to the function. In this case, the shape is an octagon just like a stop sign. The last step is to run the binary image through blob analysis to get the ROIs that are of a specified minimum size. Once the ROIs have been found, they are passed into the cascade object detector for verification of
The training data contained both labeled data D_la={〖x_i,y_i}〗_(i=1)^kl and unlabeled data D_un= {〖x_j}〗_(j=kl+1)^(kl+u) where x_(i ) is the feature descriptor of image I and y_i={1,…,k} is its label .k is the number of categories. l is the number of labeled data in each category, and u is the number of unlabeled data. Our method aims to learn a high-level image representation S by exploiting the few labeled data D_land great quantities of unlabeled ones, which is then fed into different classifiers to obtain final classification results. The procedure of semisupervised feature learning by SSEP is shown in Fig. 1. First, a new sampling algorithm based on GNA [19] is proposed to produce T WT sets P^t={(〖s_i^t,c_i^t)}〗_(i=1)^kp , t ∈{1,…..,T}
Modules have been created to deal with the huge datasets and to bring out unique insights
In the second scenario Iris dataset, which is one of the most common standard datasets is used. It consists of four attributes, 150 training samples, 150 testing samples, three classes, and three outputs as shown in Tables (\ref{Table:DatasetDescription}). The results of this dataset are summarized in Table (\ref{Table:Results}).
Gait identification has recently gained attention as a method of identifying individuals at a distance. It utilizing gait as identification for the certain distinct phases and stances. Human gait analysis is applicable for different areas like surveillance, medical diagnosis, car parking, banks, and video communication etc. It can be detected in low resolution video and it recognizable from distance. Gait identification is a term used in computer vision community to refer automatic extraction of visuals cues that characterize the motion of a walking person in video for identification purposes. For the gait image, gait feature extraction is extracted by horizontal alignments.
The goal of action recognition is to automatically analyze current activities from an unknown video [2]. Among the different recognition techniques, two main questions arise: "Which action and the location of video ?" (like recognition and location problem) . In trying to recognize human activities, one must determine the kinetic states of a person, so that the computer can effectively recognize this activity. In daily life Human activities which are relatively easy to recognize are walking and running. On the other hand, more complex activities, such as "peeling an apple", are more difficult to identify. Complex activities can be broken down into other, simpler activities that are generally easier to recognize. Fig 1, shows the decomposition
In case of road anomalies, bump testing samples were correctly recognized with accuracy 92.3\%. For driving behaviors, normal driving behaviors testing samples were correctly classified with accuracy 95.3\% while the abnormal behaviors classified with accuracy 98\%. In this experiment, k-NN achieves total recognition rate 95.9\% in determining the type of driving event. k-NN results are shown in Figure \ref{fig:algorithms}.
There are a series of images of a staring, mustachioed man who has been driven insane by the experiments, as well as what could be a guard escorting him to his cell. These images could be interpreted as occurring in real-time (as in a typical continuous scene composed of moving images), or as occurring over the course of many disparate moments in time (as in a montage sequence), or a single moment in time (as in a freeze frame). As such, it is very difficult to determine the temporal relationship of one shot to another.
Despite the controversy and debate that surround the film, one can easily see the merits of the film’s cinematography that earned Inception the “Oscar.” The film contains visual elements that leave the audience in awe, as it takes the viewer into a dream world that has not been explored by many other big motion pictures. The dream sequences we observe are in every way larger than life, yet the cinematography makes them also seem tangible to the viewer. In the scene of the dream sequence that takes place in Paris, Cobb (Leonardo
Each learing or training methods in supervised learing depends on the idea display data training in front of the network in the form of a pair of forms input form and target form. (See Figure (2-5)) .
The ChantSR class simplifies the process of recognizing speech by handling the low-level activities directly with a recognizer
Local feature methods are entirely based on descriptors of local regions in a video, no prior knowledge about human positioning nor of any of its limbs is given. In the following subsections, these categories are discussed in further.
“Video texture” is a new media introduced by Schodl et al.[23] and it is simply a video that plays forever, which can be used as a picture that moves. The problem areas associated with video texture generation are similarity measurement between frames or regions within frames, segmentation of re- gions, and the
bi-modal data, such as image and text, into a low-dimensional common space by deploying two deep