30Hz Object Detection with DPM V5
نویسندگان
چکیده
We describe an implementation of the Deformable Parts Model [1] that operates in a user-defined time-frame. Our implementation uses a variety of mechanism to trade-off speed against accuracy. Our implementation can detect all 20 PASCAL 2007 objects simultaneously at 30Hz with an mAP of 0.26. At 15Hz, its mAP is 0.30; and at 100Hz, its mAP is 0.16. By comparison the reference implementation of [1] runs at 0.07Hz and mAP of 0.33 and a fast GPU implementation runs at 1Hz. Our technique is over an order of magnitude faster than the previous fastest DPM implementation. Our implementation exploits a series of important speedup mechanisms. We use the cascade framework of [3] and the vector quantization technique of [2]. To speed up feature computation, we compute HOG features at few scales, and apply many interpolated templates. A hierarchical vector quantization method is used to compress HOG features for fast template evaluation. An object proposal step uses hash-table methods to identify locations where evaluating templates would be most useful; these locations are inserted into a priority queue, and processed in a detection phase. Both proposal and detection phases have an any-time property. Our method applies to legacy templates, and no retraining is required.
منابع مشابه
Deep Deformable Part Models
The deformable parts model (DPM) [6] serves as a key component in most modern state-of-the-art object detection systems. At a high level, the DPM composes a single object model by learning to detect and assemble parts of an object. Most modern systems employing the DPM employ densely computed Histogram of Oriented Gradients [5] features at training time. Despite the success of HOG features in m...
متن کاملStacked Deformable Part Model with Shape Regression for Object Part Localization
This paper explores the localization of pre-defined semantic object parts, which is much more challenging than traditional object detection and very important for applications such as face recognition, HCI and fine-grained object recognition. To address this problem, we make two critical improvements over the widely used deformable part model (DPM). The first is that we use appearance based sha...
متن کاملCombining Object Detectors Using Learning to Rank
Object detection is an important research area in the field of computer vision. Many detection algorithms have been proposed. However, each object detector relies on specific assumptions of the object appearance and imaging conditions. As a consequence, no algorithm can be considered universal. With the large variety of object detectors, the subsequent question is how to select and combine them...
متن کاملNon-rectangular Part Discovery for Object Detection
The deformable part-based model (DPM) is one of the most influential models for generic object detection and many efforts have been made to improve the model. Despite previous work, the problem of how to identify discriminative parts for DPM still remains largely unexplored. Most DPM based methods rely on a fixed number of parts of rectangular shapes, which may not be optimal for some object ca...
متن کاملUsing the Deformable Part Model with Autoencoded Feature Descriptors for Object Detection
Future considerations would include ne tuning the latent SVM used to train the DPM, using stacked autoencoders to learn more complex feature representations, and optimize runtime of training algorithm to allow for larger training sets. Acknowledgements Special thanks to Professor Andrew Ng and Adam Coates for the advice they provided through the course of this project. Given its performance in ...
متن کامل