Abstract
Accurate and fast object recognition is crucial in applications such as automatic driving and unmanned aerial vehicles. Traditional object recognition methods relying on image-wise computations cannot afford such real-time applications. Object proposal methods appear to fit into this scenario by segmenting object-like regions to be further analyzed by sophisticated recognition models. Traditional object proposal methods have the drawback of generating many proposals in order to maintain a satisfactory recall of true objects. This paper presents two proposal refinement strategies based on low-level cues and context-dependent features, respectively. The low-level cues are used to enhance the edge image, while the context-dependent features are verified to rule out false objects that are irrelevant to our application. In particular, the context of the drink commodity is considered because the drink commodity has the largest sales in Taiwan’s convenience store chains, and the analysis of its context has great value in marketing and management. We further developed a support vector machine (SVM) based on the Bag of Words (BoW) model with scale-invariant feature transform (SIFT) descriptors to recognize the proposals. The experimental results show that our object proposal method generates many fewer proposals than those generated by Selective Search and EdgeBoxes, with similar recall. For the performance of SVM, at least 82% of drink objects are correctly recognized for test datasets of various challenging difficulties.
Highlights
Traditional object recognition techniques require computations for extracting local features among pixels or salient points, such that high recognition accuracy can be obtained by conducting matching between the target object and an exhaustive set of candidate regions containing the local features.this sort of image-wise feature description technique, e.g., Hough transform [1] and scalable recognition [2], entails numerous computations
In order to realize which object appearance criteria present the highest challenges to our method, we manually divide these images into three test subsets by reference to criteria such as the object size, viewing angle, occlusion and truncation ratio, illumination variation, and background complexity
Object proposal generation is emerging as a mandatory form of preprocessing for efficient proposed
Summary
Traditional object recognition techniques require computations for extracting local features among pixels or salient points, such that high recognition accuracy can be obtained by conducting matching between the target object and an exhaustive set of candidate regions containing the local features. This sort of image-wise feature description technique, e.g., Hough transform [1] and scalable recognition [2], entails numerous computations. The aim of generating object proposals is to reduce the computations for pixel-by-pixel matching between the sought object and the entire image/video, but only focusing on the candidate object proposals that are deemed to contain the target object. An ideal object proposal generator improves the recall and produces fewer object proposals
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