## Temporal Dynamics between Concepts in Complex Events

Paper: | Recognition of Complex Events: Exploiting Temporal Dynamics between Underlying Concepts |

Contact: | Subhabrata Bhattacharya, Mahdi M, Kalayeh, Rahul Sukthankar, Mubarak Shah |

While approaches based on bags of features excel at lowlevel action classification, they are ill-suited for recognizing complex events in video, where concept-based temporal representations currently dominate. This paper proposes a novel representation that captures the temporal dynamics of windowed mid-level concept detectors in order to improve complex event recognition. We first express each video as an ordered vector time series, where each time step consists of the vector formed from the concatenated confidences of the pre-trained concept detectors. We hypothesize that the dynamics of time series for different instances from the same event class, as captured by simple linear dynamical system (LDS) models, are likely to be similar even if the instances differ in terms of low-level visual features. We propose a two-part representation composed of fusing: (1) a singular value decomposition of block Hankel matrices (SSID-S) and (2) a harmonic signature (HS) computed from the corresponding eigen-dynamics matrix. The proposed method offers several benefits over alternate approaches: our approach is straightforward to implement, directly employs existing concept detectors and can be plugged into linear classification frameworks. Results on standard datasets such as NIST’s TRECVID Multimedia Event Detection task demonstrate the improved accuracy of the proposed method.

In our approach, a video is decomposed into a sequence of overlapping fixed-length temporal clips, on which lowlevel feature detectors are applied. Each clip is then represented as a histogram (bag-of-visual-words) which is used as a clip level feature and tested against a set of pre-trained action concept detectors. Real-valued confidence scores, pertaining to the presence of each concept are recorded for each clip, converting the video into a vector time series. Fig. 2 illustrates sample vector time-series from different event classes through time. We model each such vector time series using a single linear dynamical system, whose characteristic properties are estimated using two different ways. The first technique (termed SSID-S) is indirect and involves computing principal projections on the Eigen decomposition of block Hankel Matrix constructed from the vector time series. The second one (termed H-S) involves directly estimating harmonic signature parameters of the LDS using a method inspired by PLiF. The representations generated by SSID-S and H-S are individually compact, discriminative and complementary, enabling us to perform late fusion in order to achieve better accuracies in complex event recognition.

Fig. 3 shows an intuitive visualization of SSID-S’s benefits over the mid-level concept feature space, computed using 100 videos from each of 5 event classes. Fig. 3 (left) shows inter-video Euclidean distance between max-pooled concept detection scores, with each concept score maxpooled temporally to generate a C-dimensional vector per video. While there is some block structure, we see significant confusion between classes (e.g., Birthday Party vs. Vehicle Unstuck). Fig. 3(right) shows the Euclidean distance matrix between videos represented using the proposed SSID signature. The latter is much cleaner, showing improved separability of event classes, even using a simple distance metric.

Fig. 4 shows an intuitive visualization of H-S’s benefits over the mid-level concept feature space, computed using the same 100 videos from each of five event classes as seen in Fig. 3. Dots (corresponding to videos) in the max-pooled concept feature space are C-dimensional, whereas those in H-S space are Cd dimensional. The scatter plot is generated by projecting each point in the feature spaces to two dimensions using PCA. We observe that the videos are much more separable in H-S space as compared to the max-pooled concept feature space: four of the five complex event classes are visually separable, even in the 2D visualization.

Software for descriptor computation from vector-time series of spatio-temporal concepts is available here. The list of detected concepts, their annotation in respective videos and relevant concept detection scores are available here.

Presentation slides are available for download. The entire talk is also shared freely, courtesy Techtalks.tv.

Subhabrata Bhattacharya, Mahdi Kalayeh, Rahul Sukthankar, Mubarak Shah, Recognition of Complex Events: Exploiting Temporal Dynamics between Underlying Concepts, In Proc. of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Columbus, USA, pp. pp-pp, 2014. **[Oral, Acceptance Rate: 5.75%]**