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This work presents a novel dense RGB-D SLAM approach for dynamic planar environments that enables simultaneous multi-object tracking, camera localisation and background reconstruction. Previous dynamic SLAM methods either rely on semantic segmentation to directly detect dynamic objects; or assume that dynamic objects occupy a smaller proportion of the camera view than the static background and can, therefore, be removed as outliers. With the aid of camera motion prior, our approach enables dense SLAM when the camera view is largely occluded by multiple dynamic objects. The dynamic planar objects are separated by their different rigid motions and tracked independently. The remaining dynamic non-planar areas are removed as outliers and not mapped into the background. The evaluation demonstrates that our approach outperforms the state-of-the-art methods in terms of localisation, mapping, dynamic segmentation and object tracking. We also demonstrate its robustness to large drift in the camera motion prior.

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Bag-of-Words (BoW) histogram of local space-time features is very popular for action representation due to its high compactness and robustness. However, its discriminant ability is limited since it only depends on the occurrence statistics of local features. Alternative models such as Vector of Locally Aggregated Descriptors (VLAD) and Fisher Vectors (FV) include more information by aggregating high-dimensional residual vectors, but they suffer from the problem of high dimensionality for final representation. To solve this problem, we novelly propose to compress residual vectors into low-dimensional residual histograms by the simple but efficient BoW quantization. To compensate the information loss of this quantization, we iteratively collect higher-order residual vectors to produce high-order residual histograms. Concatenating these histograms yields a hierarchical BoW (HBoW) model which is not only compact but also informative. In experiments, the performances of HBoW are evaluated on four benchmark datasets: HMDB51, Olympic Sports, UCF Youtube and Hollywood2. Experiment results show that HBoW yields much more compact action representation than VLAD and FV, without sacrificing recognition accuracy. Comparisons with state-of-the-art works confirm its superiority further.