Motion Estimation for Objects Analysis and Detection in Videos

Favorskaya, Margarita N.

doi:10.1007/978-3-642-24693-7_8

Cited by 8 publications

(2 citation statements)

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“…Above all, any motion in a scene ought to be analyzed on a subject of textural/non-textural regions. For this goal, the wellknown techniques, such as background subtraction, blockmatching algorithm, optical flow, or their multiple modifications may be applied (Favorskaya, 2012).…”

Section: Preliminary Categorization Of Dynamic Texturesmentioning

confidence: 99%

Convolutional Recognition of Dynamic Textures With Preliminary Categorization

Favorskaya

Pyataeva

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Dynamic Texture (DT) can be considered as an extension of the static texture additionally comprising the motion features. The DT is very wide but the weak studied type of textures that is employed in many tasks of computer vision. The proposed method of the DTs recognition includes a preliminary categorization based on the proposed four categories, such as natural particles with periodic movement, natural translucency/transparent non-rigid blobs with randomly changed movement, man-made opaque rigid objects with periodic movement, and man-made opaque rigid objects with stationary or chaotic movement. Such formulation permitted to construct the separate spatial and temporal Convolutional Neural Networks (CNNs) for each category. The inputs of the CNNs are a pair of successive frames (taken through 1, 2, 3, or 4 frames according to a category), while the outputs store the sets of binary features in a view of histograms. In test stage, the concatenated histograms are compared with the histograms of the classes using the Kullback-Leibler distance. The experiments demonstrate the efficiency of the designed CNNs and provided the recognition rates up 97.46-98.32% for the sequences with a single type of the DT conducted on the DynTex database.

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Section: Preliminary Categorization Of Dynamic Texturesmentioning

confidence: 99%

Convolutional Recognition of Dynamic Textures With Preliminary Categorization

Favorskaya

Pyataeva

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…It will be reasonable to find Regions Of Interest (ROIs) or pose primitives accurately. If a video sequence is available, then this issue is the solved problem, when the ROIs are detected from the temporal domain in previous frames (Favorskaya, 2012;Favorskaya et al, 2015). Also the hybrid methods based on optical flow and learning of salient regions are possible (e.g., Eweiwi et al 2015).…”

Section: Imposed Grid For Poselet Detectionmentioning

confidence: 99%

Human Action Poselets Estimation via Color G-Surf in Still Images

Favorskaya

Novikov

Savitskaya

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Human activity is a persistent subject of interest in the last decade. On the one hand, video sequences provide a huge volume of motion information in order to recognize the human active actions. On the other hand, the spatial information about static human poses is valuable for human action recognition. Poselets were introduced as latent variables representing a configuration for mutual locations of body parts and allowing different views of description. In current research, some modifications of Speeded-Up Robust Features (SURF) invariant to affine geometrical transforms and illumination changes were tested. First, a grid of rectangles is imposed on object of interest in a still image. Second, sparse descriptor based on Gauge-SURF (G-SURF) invariant to color/lighting changes is constructed for each rectangle separately. A common Spatial POselet Descriptor (SPOD) aggregates the SPODs of rectangles with following random forest classification in order to receive fast classification results. The proposed approach was tested on samples from PASCAL Visual Object Classes (VOC) Dataset and Challenge 2010 providing accuracy 61-68% for all possible 3D poses locations and 82-86% for front poses locations regarding to nine action categories.

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