23,866 research outputs found
ALFA: Agglomerative Late Fusion Algorithm for Object Detection
We propose ALFA - a novel late fusion algorithm for object detection. ALFA is
based on agglomerative clustering of object detector predictions taking into
consideration both the bounding box locations and the class scores. Each
cluster represents a single object hypothesis whose location is a weighted
combination of the clustered bounding boxes.
ALFA was evaluated using combinations of a pair (SSD and DeNet) and a triplet
(SSD, DeNet and Faster R-CNN) of recent object detectors that are close to the
state-of-the-art. ALFA achieves state of the art results on PASCAL VOC 2007 and
PASCAL VOC 2012, outperforming the individual detectors as well as baseline
combination strategies, achieving up to 32% lower error than the best
individual detectors and up to 6% lower error than the reference fusion
algorithm DBF - Dynamic Belief Fusion.Comment: E. Razinkov, I. Saveleva and J. Matas, "ALFA: Agglomerative Late
Fusion Algorithm for Object Detection," 2018 24th International Conference on
Pattern Recognition (ICPR), Beijing, 2018, pp. 2594-259
Closed-loop Bayesian Semantic Data Fusion for Collaborative Human-Autonomy Target Search
In search applications, autonomous unmanned vehicles must be able to
efficiently reacquire and localize mobile targets that can remain out of view
for long periods of time in large spaces. As such, all available information
sources must be actively leveraged -- including imprecise but readily available
semantic observations provided by humans. To achieve this, this work develops
and validates a novel collaborative human-machine sensing solution for dynamic
target search. Our approach uses continuous partially observable Markov
decision process (CPOMDP) planning to generate vehicle trajectories that
optimally exploit imperfect detection data from onboard sensors, as well as
semantic natural language observations that can be specifically requested from
human sensors. The key innovation is a scalable hierarchical Gaussian mixture
model formulation for efficiently solving CPOMDPs with semantic observations in
continuous dynamic state spaces. The approach is demonstrated and validated
with a real human-robot team engaged in dynamic indoor target search and
capture scenarios on a custom testbed.Comment: Final version accepted and submitted to 2018 FUSION Conference
(Cambridge, UK, July 2018
Multispectral object segmentation and retrieval in surveillance video
This paper describes a system for object segmentation and feature extraction for surveillance video. Segmentation is performed by a dynamic vision system that fuses information from thermal infrared video with standard CCTV video in order to detect and track objects. Separate background modelling in each modality and dynamic mutual information based thresholding are used to provide initial foreground candidates for tracking. The belief in the validity of these candidates is ascertained using knowledge of foreground pixels and temporal linking of candidates. The transferable belief model is used to combine these sources of information and segment objects. Extracted objects are subsequently tracked using adaptive thermo-visual appearance models. In order to facilitate search and classification of objects in large archives, retrieval features from both modalities are extracted for tracked objects. Overall system performance is demonstrated in a simple retrieval scenari
Time-Domain Data Fusion Using Weighted Evidence and DempsterâShafer Combination Rule: Application in Object Classification
To apply data fusion in time-domain based on DempsterâShafer (DS) combination rule, an 8-step algorithm with novel entropy function is proposed. The 8-step algorithm is applied to time-domain to achieve the sequential combination of time-domain data. Simulation results showed that this method is successful in capturing the changes (dynamic behavior) in time-domain object classification. This method also showed better anti-disturbing ability and transition property compared to other methods available in the literature. As an example, a convolution neural network (CNN) is trained to classify three different types of weeds. Precision and recall from confusion matrix of the CNN are used to update basic probability assignment (BPA) which captures the classification uncertainty. Real data of classified weeds from a single sensor is used test time-domain data fusion. The proposed method is successful in filtering noise (reduce sudden changesâsmoother curves) and fusing conflicting information from the video feed. Performance of the algorithm can be adjusted between robustness and fast-response using a tuning parameter which is number of time-steps(ts)
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