Popular Hough Transform-based object detection approaches usually construct an appearance codebook by clustering local image features. However, how to choose appropriate values for the parameters used in the clustering step remains an open problem. Moreover, some popular histogram features extracted from overlapping image blocks may cause a high degree of redundancy and multicollinearity. In this paper, we propose a novel Hough Transform-based object detection approach. First, to address the above issues, we exploit a Bridge Partial Least Squares (BPLS) technique to establish context-encoded Hough Regression Models (HRMs), which are linear regression models that cast probabilistic Hough votes to predict object locations. BPLS is an efficient variant of Partial Least Squares (PLS). PLS-based regression techniques (including BPLS) can reduce the redundancy and eliminate the multicollinearity of a feature set. And the appropriate value of the only parameter used in PLS (i.e., the number of latent components) can be determined by using a cross-validation procedure. Second, to efficiently handle object scale changes, we propose a novel multi-scale voting scheme. In this scheme, multiple Hough images corresponding to multiple object scales can be obtained simultaneously. Third, an object in a test image may correspond to multiple true and false positive hypotheses at different scales. Based on the proposed multi-scale voting scheme, a principled strategy is proposed to fuse hypotheses to reduce false positives by evaluating normalized pointwise mutual information between hypotheses. In the experiments, we also compare the proposed HRM approach with its several variants to evaluate the influences of its components on its performance. Experimental results show that the proposed HRM approach has achieved desirable performances on popular benchmark datasets.
As for handling object scale variations, the ISM @cite_15 , 4D-ISM @cite_14 , partISM @cite_23 , IRD @cite_6 , Fast PRISM @cite_2 and MMHT @cite_11 approaches use local feature descriptors to estimate the scales of local features and cast Hough votes in a scale space. The positions where the voting points are most concentrated in the scale space are considered as the locations of object hypotheses. The Hough forests @cite_22 , PSCG @cite_20 and latent Hough forest @cite_31 approaches, as well as the iterative multi-object extraction framework @cite_7 , simply rescale a test image to form an image pyramid and perform object detection at each level of the pyramid. In the derived Hough image pyramid, 3D local maxima indicate the estimated positions and scales of object hypotheses @cite_22 . In @cite_18 , local scale estimation is considered to be unreliable. Therefore, to solve this problem, voting points are extended to voting lines in @cite_18 . A voting line consists of the voting points cast by a local feature at all scales in a scale space. The position of an intersection point of voting lines in the scale space indicates the location and scale of an object hypothesis.
@cite_31 @cite_14 @cite_18 @cite_22 @cite_7 @cite_6 @cite_23 @cite_2 @cite_15 @cite_20 @cite_11
1777102342 2548546665 2168373637 2125556102
Hough voting methods efficiently handle the high complexity of multiscale, category-level object detection in cluttered scenes. The primary weakness of this approach is however that mutually dependent local observations are independently voting for intrinsically global object properties such as object scale. All the votes are added up to obtain object hypotheses. The assumption is thus that object hypotheses are a sum of independent part votes. Popular representation schemes are, however, based on an overlapping sampling of semi-local image features with large spatial support (e.g. SIFT or geometric blur). Features are thus mutually dependent and we incorporate these dependences into probabilistic Hough voting by presenting an objective function that combines three intimately related problems: i) grouping of mutually dependent parts, ii) solving the correspondence problem conjointly for dependent parts, and iii) finding concerted object hypotheses using extended groups rather than based on local observations alone. Experiments successfully demonstrate that state-of-the-art Hough voting and even sliding windows are significantly improved by utilizing part dependences and jointly optimizing groups, correspondences, and votes. Object detection in cluttered, natural scenes has a high complexity since many local observations compete for object hypotheses. Voting methods provide an efficient solution to this problem. When Hough voting is extended to location and scale, votes naturally become lines through scale space due to the local scale-location-ambiguity. In contrast to this, current voting methods stick to the location-only setting and cast point votes, which require local estimates of scale. Rather than searching for object hypotheses in the Hough accumulator, we propose a weighted, pairwise clustering of voting lines to obtain globally consistent hypotheses directly. In essence, we propose a hierarchical approach that is based on a sparse representation of object boundary shape. Clustering of voting lines (CVL) condenses the information from these edge points in few, globally consistent candidate hypotheses. A final verification stage concludes by refining the candidates. Experiments on the ETHZ shape dataset show that clustering voting lines significantly improves state-of-the-art Hough voting techniques. This paper addresses the task of efficient object class detection by means of the Hough transform. This approach has been made popular by the Implicit Shape Model (ISM) and has been adopted many times. Although ISM exhibits robust detection performance, its probabilistic formulation is unsatisfactory. The PRincipled Implicit Shape Model (PRISM) overcomes these problems by interpreting Hough voting as a dual implementation of linear sliding-window detection. It thereby gives a sound justification to the voting procedure and imposes minimal constraints. We demonstrate PRISM's flexibility by two complementary implementations: a generatively trained Gaussian Mixture Model as well as a discriminatively trained histogram approach. Both systems achieve state-of-the-art performance. Detections are found by gradient-based or branch and bound search, respectively. The latter greatly benefits from PRISM's feature-centric view. It thereby avoids the unfavourable memory trade-off and any on-line pre-processing of the original Efficient Subwindow Search (ESS). Moreover, our approach takes account of the features' scale value while ESS does not. Finally, we show how to avoid soft-matching and spatial pyramid descriptors during detection without losing their positive effect. This makes algorithms simpler and faster. Both are possible if the object model is properly regularised and we discuss a modification of SVMs which allows for doing so. Multi-resolution image features may be approximated via extrapolation from nearby scales, rather than being computed explicitly. This fundamental insight allows us to design object detection algorithms that are as accurate, and considerably faster, than the state-of-the-art. The computational bottleneck of many modern detectors is the computation of features at every scale of a finely-sampled image pyramid. Our key insight is that one may compute finely sampled feature pyramids at a fraction of the cost, without sacrificing performance: for a broad family of features we find that features computed at octave-spaced scale intervals are sufficient to approximate features on a finely-sampled pyramid. Extrapolation is inexpensive as compared to direct feature computation. As a result, our approximation yields considerable speedups with negligible loss in detection accuracy. We modify three diverse visual recognition systems to use fast feature pyramids and show results on both pedestrian detection (measured on the Caltech, INRIA, TUD-Brussels and ETH data sets) and general object detection (measured on the PASCAL VOC). The approach is general and is widely applicable to vision algorithms requiring fine-grained multi-scale analysis. Our approximation is valid for images with broad spectra (most natural images) and fails for images with narrow band-pass spectra (e.g., periodic textures).