A novel approach for the fusion of heterogeneous object detection methods is proposed. In order to effectively integrate the outputs of multiple detectors, the level of ambiguity in each individual detection score is estimated using the precision recall relationship of the corresponding detector. The main contribution of the proposed work is a novel fusion method, called Dynamic Belief Fusion (DBF), which dynamically assigns probabilities to hypotheses (target, non-target, intermediate state (target or non-target)) based on confidence levels in the detection results conditioned on the prior performance of individual detectors. In DBF, a joint basic probability assignment, optimally fusing information from all detectors, is determined by the Dempster's combination rule, and is easily reduced to a single fused detection score. Experiments on ARL and PASCAL VOC 07 datasets demonstrate that the detection accuracy of DBF is considerably greater than conventional fusion approaches as well as individual detectors used for the fusion.
In the case where modeling dependency among multiple approaches is not possible, fusion can be performed over their outputs (late fusion). @cite_18 introduced a fusion framework in the target tracking paradigm. They collected trajectories from multiple tracking algorithms and computed one fused trajectory in order to improve accuracy, trajectory continuity, and smoothness. However, since temporal information obtained from trajectory cannot be applicable in the object detection task, a different fusion framework is necessary for our problem. @cite_23 and @cite_8 used weighted-sum (WS) methods to fuse multiple types of data for object detection. Their WS method learns weights in a manner that estimates trust in multiple data sources. However, since weights optimization is usually performed to maximize distance between positive and negative samples, like Bayesian fusion, WS does not provide a way to indicate ambiguity between the positives and negatives, which degrades fusion performance, as previously mentioned. The works of Ma and Yuen @cite_5 and @cite_11 employing Bayesian fusion also shows limited performance.
@cite_18 @cite_8 @cite_23 @cite_5 @cite_11
2543366260 2106944077 2016802777 2588362028
In this paper, we introduce a novel fusion method that can enhance object detection performance by fusing decisions from two different types of computer vision tasks: object detection and image classification. In the proposed work, the class label of an image obtained from image classification is viewed as prior knowledge about existence or non-existence of certain objects. The prior knowledge is then fused with the decisions of object detection to improve detection accuracy by mitigating false positives of an object detector that are strongly contradicted with the prior knowledge. A recently introduced novel fusion approach called dynamic belief fusion (DBF) is used to fuse the detector output with the classification prior. Experimental results show that the detection performance of all the detection algorithms used in the proposed work is improved on benchmark datasets via the proposed fusion framework. Information from multiple heterogenous data sources (e.g. visible and infrared) or representations (e.g. intensity and edge) have become increasingly important in many video-based applications. Fusion of information from these sources is critical to improve the robustness of related visual information processing systems. In this paper we propose a data fusion approach via sparse representation with applications to robust visual tracking. Specifically, the image patches from different sources of each target candidate are concatenated into a one-dimensional vector that is then sparsely represented in the target template space. The template space representation, which naturally fuses information from different sources, brings several benefits to visual tracking. First, it inherits robustness to appearance contaminations from the previously proposed sparse trackers. Second, it provides a flexible framework that can easily integrate information from different data sources. Third, it can be used for handling various number of data sources, which is very useful for situations where the data inputs arrive at different frequencies. The sparsity in the representation is achieved by solving an l1-regularized least squares problem. The tracking result is then determined by finding the candidate with the smallest approximation error. To propagate the results over time, the sparse solution is combined with the Bayesian state inference framework using the particle filter algorithm. We conducted experiments on several real videos with heterogeneous information sources. The results show that the proposed approach can track the target more robustly than several state-of-the-art tracking algorithms. Currently sparse signal reconstruction gains considerable interest and is applied in many fields. In this paper, a similarity induced by joint sparse representation is designed to construct the likelihood function of particle filter tracker so that the color visual spectrum and thermal spectrum images can be fused for object tracking. The proposed fusion scheme performs joint sparse representation calculation on both modalities and the resultant tracking results are fused using min operation on the sparse representation coefficients. In addition, a co-learning approach is proposed to update the reference templates of both modality and enhance the tracking robustness. The proposed fusion scheme outperforms state-of-the-art approaches, and its effectiveness is verified using OTCBVS database. This paper presents an approach to enable general tracking of extended objects of multiple sensors. Expert information in each input providing sensor module is mapped into simple model parameters and allows the fusion center to use a generalized version of such information. The model type and parameters are presented and a classical Kalman based fusion is extended with a method for integrated extent handling. By this approach the central fusion node can take into account both the track level information and extent estimate from each sensor. The proposed method is compared with a classic method of fusing the object center and the extent estimate separately. Simulated data is used to show that our proposed approach is general in the sense that it can be used on various setups without adaption. Detailed performance results are given based on estimation errors of the extended object space vector. The findings based on simulated data are completed by real world data from a front facing sensor setup. It is shown that the proposed method offers a benefit in position accuracy, especially when the measurement information does not contain a complete extent information in all directions.