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Here, the authors focus on incrementally acquiring heterogeneous knowledge from both internet and publicly available datasets to reduce the tedious and expensive labelling efforts required in video annotation. An incremental transfer learning framework is presented to integrate heterogeneous source knowledge and update the annotation model incrementally during the transfer learning process. Under this framework, web images and existing action videos form the source domain to provide labelled static and motion information of the target domain videos, respectively. Moreover, according to the semantic of the source domain data, all the source domain data are partitioned into several groups. Different from traditional methods, which compare the entire target domain videos with each source group from the source domain, the authors treat the group weights as sample‐specific variables and optimise them along with new adding data. Two regularisers are used to prevent the incremental learning process from negative transfer. Experimental results on the two large‐scale consumer video datasets (i.e. multimedia event detection (MED) and Columbia consumer video (CCV)) show the effectiveness of the proposed method.

Object representations are of great importance for robust visual tracking. Although the high‐dimensional representation can effectively encode the input data with more information, exploiting it in a real‐time tracking system would be intractable and infeasible due to the high computational cost and memory requirements. In this study, the authors propose a compact discriminative object representation to achieve both good tracking accuracy and efficiency. An ensemble of weak training sets is generated based on the self‐representative ability of tracking samples, which is applied to learn discriminative functions. Each candidate is represented by the concatenation of project values on all the weak training sets. Tracking is then carried out within a Bayesian inference framework where the classification score of the support vector machine is used to construct the observation model. The evaluations on TB50 benchmark dataset demonstrate that the proposed algorithm is much more computationally efficient than the state‐of‐the‐art methods with comparable accuracy.

In this study, the authors study the video annotation problem over heterogeneous domains, in which data from the image source domain and the video target domain is represented by heterogeneous features with different dimensions and physical meanings. A novel feature learning method, called heterogeneous discriminative analysis of canonical correlation (HDCC), is proposed to discover a common feature subspace in which heterogeneous features can be compared. The HDCC utilises discriminative information from the source domain as well as topology information from the target domain to learn two different projection matrices. By using these two matrices, heterogeneous data can be projected onto a common subspace and different features can be compared. They additionally design a group weighting learning framework for multi‐domain adaptation to effectively leverage knowledge learned from the source domain. Under this framework, source domain images are organised in groups according to their semantic meanings, and different weights are assigned to these groups according to their relevancies to the target domain videos. Extensive experiments on the Columbia Consumer Video and Kodak datasets demonstrate the effectiveness of their HDCC and group weighting methods.

In this study, the authors propose a multi‐group–multi‐class domain adaptation framework to recognise events in consumer videos by leveraging a large number of web videos. The authors’ framework is extended from multi‐class support vector machine by adding a novel data‐dependent regulariser, which can force the event classifier to become consistent in consumer videos. To obtain web videos, they search them using several event‐related keywords and refer the videos returned by one keyword search as a group. They also leverage a video representation which is the average of convolutional neural networks features of the video frames for better performance. Comprehensive experiments on the two real‐world consumer video datasets demonstrate the effectiveness of their method for event recognition in consumer videos.

The performance of online visual trackers has improved significantly, but designing an effective appearance‐adaptive model is still a challenging task because of the accumulation of errors during the model updating with newly obtained results, which will cause tracker drift. In this study, the authors propose a novel online tracking algorithm by integrating spatio‐temporal cues to alleviate the drift problem. The authors' goal is to develop a more robust way of updating an adaptive appearance model. The model consists of multiple modules called temporal cues, and these modules are updated in an alternate way which can keep both the historical and current information of the tracked object to handle drastic appearance change. Each module is represented by several fragments called spatial cues. In order to incorporate all the spatial and temporal cues, the authors develop an efficient cue quality evaluation criterion that combines appearance and motion information. Then the tracking results are obtained by a two‐stage dynamic integration mechanism. Both qualitative and quantitative evaluations on challenging video sequences demonstrate that the proposed algorithm performs more favourably against the state‐of‐the‐art methods.

This study addresses the problem of recognising human interactions between two people. The main difficulties lie in the partial occlusion of body parts and the motion ambiguity in interactions. The authors observed that the interdependencies existing at both the action level and the body part level can greatly help disambiguate similar individual movements and facilitate human interaction recognition. Accordingly, they proposed a novel discriminative method, which model the action of each person by a large‐scale global feature and local body part features, to capture such interdependencies for recognising interaction of two people. A variant of multi‐class Adaboost method is proposed to automatically discover class‐specific discriminative three‐dimensional body parts. The proposed approach is tested on the authors newly introduced BIT‐interaction dataset and the UT‐interaction dataset. The results show that their proposed model is quite effective in recognising human interactions.