We present an novel approach to activity analysis, which is based on primitive features that encode pure motion. These are coupled with a hierarchical scheme to learn motion patterns (compositions) from a single short video. During the inference process, these learned patterns are extracted from the analyzed videos and used with chi-square SVM classifier in a ”bag of compositions” approach. The process is computationally efficient and the method is well-suited for implementation on massively parallel architectures. Due to their compositional nature, motion patterns can be trained incrementally (layer by layer) and stored efficiently. Inference is fast and the final feature vectors are of relatively low dimension, thus enabling fast SVM training. On the standard UCF Sports Action Dataset, the presented method outperforms state-of-the art approaches based on pure-motion.
This paper focuses on applying and evaluating the additional hypothesis verification step for the detections of learnt hierarchy- of-parts (LHOP) method. The applied method reduces the problem of false positives that are a common problem of hierarchical methods specifically in highly textured or cluttered images. We use a Histogram of Compositions (HoC) with a Support Vector Machine in hypothesis verification step. Using HoC descriptor ensures that the additional computation cost is as minimal as possible since HoC descriptor shares the LHOP tree structure. We evaluate the method on the ETHZ Shape Classes dataset and show that our method outperforms the original baseline LHOP method by around 5 percent.
Computational modeling of the primate visual system yields insights of potential relevance to some of the challenges that computer vision is facing, such as object recognition and categorization, motion detection and activity recognition, or vision-based navigation and manipulation. This paper reviews some functional principles and structures that are generally thought to underlie the primate visual cortex, and attempts to extract biological principles that could further advance computer vision research. Organized for a computer vision audience, we present functional principles of the processing hierarchies present in the primate visual system considering recent discoveries in neurophysiology. The hierarchical processing in the primate visual system is characterized by a sequence of different levels of processing that constitute a deep hierarchy in contrast to the flat vision architectures predominantly used in today’s mainstream computer vision. The functional description of the deep hierarchies realized in the primate visual system provides valuable insights for the design of computer vision algorithms, fostering increasingly productive interaction between biological and computer vision research.