9,969 research outputs found
Gait recognition and understanding based on hierarchical temporal memory using 3D gait semantic folding
Gait recognition and understanding systems have shown a wide-ranging application prospect. However, their use of unstructured data from image and video has affected their performance, e.g., they are easily influenced by multi-views, occlusion, clothes, and object carrying conditions. This paper addresses these problems using a realistic 3-dimensional (3D) human structural data and sequential pattern learning framework with top-down attention modulating mechanism based on Hierarchical Temporal Memory (HTM). First, an accurate 2-dimensional (2D) to 3D human body pose and shape semantic parameters estimation method is proposed, which exploits the advantages of an instance-level body parsing model and a virtual dressing method. Second, by using gait semantic folding, the estimated body parameters are encoded using a sparse 2D matrix to construct the structural gait semantic image. In order to achieve time-based gait recognition, an HTM Network is constructed to obtain the sequence-level gait sparse distribution representations (SL-GSDRs). A top-down attention mechanism is introduced to deal with various conditions including multi-views by refining the SL-GSDRs, according to prior knowledge. The proposed gait learning model not only aids gait recognition tasks to overcome the difficulties in real application scenarios but also provides the structured gait semantic images for visual cognition. Experimental analyses on CMU MoBo, CASIA B, TUM-IITKGP, and KY4D datasets show a significant performance gain in terms of accuracy and robustness
Annotating Object Instances with a Polygon-RNN
We propose an approach for semi-automatic annotation of object instances.
While most current methods treat object segmentation as a pixel-labeling
problem, we here cast it as a polygon prediction task, mimicking how most
current datasets have been annotated. In particular, our approach takes as
input an image crop and sequentially produces vertices of the polygon outlining
the object. This allows a human annotator to interfere at any time and correct
a vertex if needed, producing as accurate segmentation as desired by the
annotator. We show that our approach speeds up the annotation process by a
factor of 4.7 across all classes in Cityscapes, while achieving 78.4% agreement
in IoU with original ground-truth, matching the typical agreement between human
annotators. For cars, our speed-up factor is 7.3 for an agreement of 82.2%. We
further show generalization capabilities of our approach to unseen datasets
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