{"content":[{"text":"features are used in an SVM classiﬁcation scheme. Blank et \n al. [2] represent an action by considering the shape carved \n by its silhouette in time. Local shape descriptors based on \n the Poisson equation are computed, then aggregated into \n a global descriptor by computing moments. Niebles et \n al. [19] quantize local space-time features and use a “bag- \n of-words” representation together with Latent Dirichlet Al- \n location to recognize actions. Rao et al. [21] track hands, \n and describe actions based on inﬂection points of these \n 2D tracks. Nowozin et al. [20] classify image sequences \n by ﬁnding discriminative subsequences of quantized local \n space-time features. Laptev and Prez [12] ﬁnd actions in \n movies. They use AdaBoost to learn a cascade of boosted \n classiﬁers from shape and motion features. They use his- \n tograms of oriented gradients and optical ﬂows to represent \n shape and motion respectively. \n We build on the mid-level shapelet features [22] in which \n low-level spatial gradient features are combined to form dis- \n criminative mid-level features. These features share sim- \n ilarity with features constructed by biologically inspired \n methods for object recognition [13, 25, 17]. The work by \n Serre et al. [25] and Mutch and Lowe [17] use many ran- \n domly sampled patches as their features. These features are \n used as templates in a feed-forward network structure con- \n taining alternating stages of template matching and pooling \n operations for invariance. \n The gradient-based learning methods [13] have a similar \n structure of alternating between layers which mimic simple \n and complex cell behaviour. LeCun et al. [13] learn a set of \n convolution kernels to be used as the simple cell features us- \n ing back-propagation. The shapelet features are more com- \n plex in structure, containing combinations of gradients over \n a mid-level scale; and also discriminatively learned from \n training data, using positive and negative class examples to \n decide on their contents. This line of work was recently ex- \n tended to action recognition by Jhuang et al. [10]. Motion \n gradients were added as a low-level feature in this model. \n In our work we show that by using motion features to build \n mid-level features in the shapelet feature framework, highly \n accurate action recognition performance, as in [10], can be \n achieved.","bbox":{"left":0.081882,"top":0.091866,"width":0.386035,"height":0.618954}},{"text":"lation tracking (soccer). \n The use of this ﬁgure-centric representation guarantees \n that we are in fact recognizing actions and not just simple \n translations. While we will have removed information that \n would certainly be useful in some surveillance settings, our \n algorithm can be directly applied to video footage captured \n with a moving camera, such as the soccer dataset. \n We will consider this sequence of ﬁgure-centric frames \n as a 3D space-time volume for future processing. We \n will build algorithms which attempt to classify short sub- \n sequences (5 frames long in experiments) of this ﬁgure- \n centric volume into low-level actions. In the exposition that \n follows, we will describe this in terms of a binary classi- \n ﬁcation task, discriminating between two classes. In Sec- \n tion 3.4 we will use standard algorithms for converting our \n true multi-class task into such binary tasks. \n The training part of our approach for learning a classi- \n ﬁer to distinguish between two different actions consists of \n three layers:","bbox":{"left":0.504676,"top":0.091866,"width":0.386035,"height":0.288224}},{"text":"1.","bbox":{"left":0.516886,"top":0.394416,"width":0.012209000000000025,"height":0.015158000000000005}},{"text":"Low-level Motion Features as Weak Classiﬁers: \n The input to this layer is a ﬁgure-centric volume. We \n compute low-level motion features on this input se- \n quence, which will be described in Section 3.1. Any \n of these features, identiﬁed by its location and motion \n direction, can be used as a weak classiﬁer of the two \n action classes by setting a threshold on its responses. \n These weak classiﬁers will be used to make more so- \n phisticated features.","bbox":{"left":0.537234,"top":0.393964,"width":0.35347700000000004,"height":0.13636900000000007}},{"text":"2.","bbox":{"left":0.516886,"top":0.542144,"width":0.012209000000000025,"height":0.015158000000000005}},{"text":"Mid-level Motion Features: For some small cuboids \n inside the ﬁgure-centric volume, we run Adaboost [27] \n to select a subset of the weak classiﬁers inside each \n ﬁgure-centric volume to construct better classiﬁers. By \n only using the features inside each cuboid, we force \n AdaBoost to extract as much information as possible \n at local neighborhoods of the image sequence. This \n process, described in Section 3.2, will provide us sev- \n eral mid-level motion features that are intended to be \n stronger local classiﬁers than the low-level features \n and discriminative regarding our action classes. Each \n mid-level feature consists of a combination of a set of \n motion directions at various locations.","bbox":{"left":0.537234,"top":0.541691,"width":0.35347700000000004,"height":0.19674899999999995}},{"text":"3. Method \n Our method for action recognition operates on a “ﬁgure- \n centric” representation of the human ﬁgure extracted from \n an input image sequence. This ﬁgure-centric representation \n is obtained by running a detection/tracking algorithm over \n the input image sequence. The input to our action recog- \n nition algorithm will be a stabilized sequence of cropped \n frames, centered on the human ﬁgure. In our experiments \n we use a variety of previously developed algorithms to con- \n struct this representation – pedestrian detection [22] (KTH), \n background subtraction (Weizmann), and normalized corre-","bbox":{"left":0.081882,"top":0.723482,"width":0.386035,"height":0.17778000000000005}},{"text":"3.","bbox":{"left":0.516886,"top":0.750251,"width":0.012209000000000025,"height":0.015158000000000005}},{"text":"Final Classiﬁer: The mid-level features only act in \n local neighborhoods of the image sequence and there- \n fore their overall classiﬁcation power is still much be- \n low an acceptable level. By merging those features to- \n gether we can combine the information from different \n parts of the image sequence. In order to archive this \n goal, we use AdaBoost for a second time to train our \n ﬁnal classiﬁer from the mid-level motion features. Ad- \n aBoost will choose the best subset of mid-level motion \n features that can separate the two action classes. The","bbox":{"left":0.537234,"top":0.749798,"width":0.35347700000000004,"height":0.151466}}],"images_bbox":[],"images_bbox_not_text_overlap":[]}