Objects In Key Frames Research Articles

Local track to detect for video object detection

The existing methods for video object detection are generally achieved from searching the objects through the entire image. However, they always suffer from large computation consumption as a result of dozens of similar images needing to be operated. To relieve this problem, we propose a Local Track to Detect (LTD) framework to detect video objects by predicting the movements of objects in local areas. LTD can automatically determine key frames and non-key frames, the objects in key frames can be detected by the single frame detector, and the objects in non-key frames can be efficiently detected by the movement prediction module. LTD also has a Siamese module to predict whether objects between the key frame and the non-key frame are the same object to ensure the accuracy of the movement prediction module. Compared with other previous work, our method is more efficient and achieves state-of-the-art performance.

Complex Object Correspondence Construction in Two-Dimensional Animation

Correspondence construction of objects in key frames is the precondition for inbetweening and coloring in 2-D computer-assisted animation production. Since each frame of an animation consists of multiple layers, objects are complex in terms of shape and structure. Therefore, existing shape-matching algorithms specifically designed for simple structures such as a single closed contour cannot perform well on objects constructed by multiple contours with an open shape. This paper introduces a semisupervised patch alignment framework for complex object correspondence construction. In particular, the new framework constructs local patches for each point on an object and aligns these patches in a new feature space, in which correspondences between objects can be detected by the subsequent clustering. For local patch construction, pairwise constraints, which indicate the corresponding points (must link) or unfitting points (cannot link), are introduced by users to improve the performance of correspondence construction. This kind of input is convenient for animation software users via user-friendly interfaces. A dozen of experimental results on our cartoon data set that is built on industrial production suggest the effectiveness of the proposed framework for constructing correspondences of complex objects. As an extension of our framework, additional shape retrieval experiments on MPEG-7 data set show that its performance is comparable with that of a prominent algorithm published in T-PAMI 2009.