We present a unified framework which supports grounding natural-language semantics in robotic driving. This framework supports acquisition (learning grounded meanings of nouns and prepositions from human sentential annotation of robotic driving paths), generation (using such acquired meanings to generate sentential description of new robotic driving paths), and comprehension (using such acquired meanings to support automated driving to accomplish navigational goals specified in natural language). We evaluate the performance of these three tasks by having independent human judges rate the semantic fidelity of the sentences associated with paths. Overall, machine performance is 74.9%, while the performance of human annotators is 83.8%.

We make available to the community a new dataset to support action recognition research. This dataset is different from prior datasets in several key ways. It is significantly larger. It contains streaming video with long segments containing multiple action occurrences that often overlap in space and/or time. All actions were filmed in the same collection of backgrounds so that background gives little clue as to action class. We had five humans to replicate the annotation of temporal extent of action occurrences labeled with their classes and measured a surprisingly low level of intercoder agreement. Baseline experiments show that recent state-of-the-art methods perform poorly on this dataset. This suggests that this will be a challenging dataset to foster advances in action recognition research. This manuscript serves to describe the novel content and characteristics of the LCA dataset, present the design decisions made when filming the dataset, document the novel methods employed to annotate the dataset, and present the results of our baseline experiments.

We present an approach to searching large video corpora for clips which depict a natural-language query in the form of a sentence. Compositional semantics is used to encode subtle meaning differences lost in other approaches, such as the difference between two sentences which have identical words but entirely different meaning: The person rode the horse versus The horse rode the person. Given a sentential query and a natural-language parser, we produce a score indicating how well a video clip depicts that sentence for each clip in a corpus and return a ranked list of clips. Two fundamental problems are addressed simultaneously: detecting and tracking objects, and recognizing whether those tracks depict the query. Because both tracking and object detection are unreliable, our approach uses the sentential query to focus the tracker on the relevant participants and ensures that the resulting tracks are described by the sentential query. While most earlier work was limited to single-word queries which correspond to either verbs or nouns, we search for complex queries which contain multiple phrases, such as prepositional phrases, and modifiers, such as adverbs. We demonstrate this approach by searching for 2,627 naturally elicited sentential queries in 10 Hollywood movies.

We present a method for recognizing and localizing actions in video by the sequence of changing appearance and motion of the participants. Appearance is modeled by histogram of oriented gradients object detectors, while motion is modeled by optical-flow motion-pattern detectors. Sequencing is modeled by a hidden Markov model (HMM) whose output models are these appearance and motion detectors. The HMM and associated detectors are simultaneously trained, learning the sequence of detectors that match the most distinctive temporal subsequences of the action represented in the training data. Training uses both positive and negative samples of a given action class and is accomplished without the need for annotation of the correspondence between training video frames and the state-conditioned detectors, by minimizing a discriminative cost function through gradient descent. Trained models are used to perform recognition and localization by simultaneous detection, tracking, and action recognition. In contrast to many prior methods, our approach learns intuitively meaningful models that represent action as a sequence of retinotopic models. We demonstrate such by rendering these models on unseen test video. This method was found to perform competitively on three standard datasets, Weizmann, KTH, and UCF Sports, as well as on the video from the Defence Advanced Research Project Agency (DARPA) Mind's Eye program and a newly filmed dataset.

Recognizing human activities in partially observed videos is a challenging problem and has many practical applications. When the unobserved subsequence is at the end of the video, the problem is reduced to activity prediction from unfinished activity streaming, which has been studied by many researchers. However, in the general case, an unobserved subsequence may occur at any time by yielding a temporal gap in the video. In this paper, we propose a new method that can recognize human activities from partially observed videos in the general case. Specifically, we formulate the problem into a probabilistic framework: 1) dividing each activity into multiple ordered temporal segments, 2) using spatiotemporal features of the training video samples in each segment as bases and applying sparse coding (SC) to derive the activity likelihood of the test video sample at each segment, and 3) finally combining the likelihood at each segment to achieve a global posterior for the activities. We further extend the proposed method to include more bases that correspond to a mixture of segments with different temporal lengths (MSSC), which can better represent the activities with large intra-class variations. We evaluate the proposed methods (SC and MSSC) on various real videos. We also evaluate the proposed methods on two special cases: 1) activity prediction where the unobserved subsequence is at the end of the video, and 2) human activity recognition on fully observed videos. Experimental results show that the proposed methods outperform existing state-of-the-art comparison methods.