This work proposes a simple pipeline to classify and temporally localize activities in untrimmed videos. Our system uses features from a 3D Convolutional Neural Network (C3D) as input to train a a recurrent neural network (RNN) that learns to classify video clips of 16 frames. After clip prediction, we post-process the output of the RNN to assign a single activity label to each video, and determine the temporal boundaries of the activity within the video. We show how our system can achieve competitive results in both tasks with a simple architecture. We evaluate our method in the ActivityNet Challenge 2016, achieving a 0.5874 mAP and a 0.2237 mAP in the classification and detection tasks, respectively. Our code and models are publicly available at: https://imatge-upc.github.io/activitynet-2016-cvprw/Peer ReviewedPostprint (published version

Giró Nieto, Xavier

Montes, Alberto

Pascual, Santiago

Salvador Aguilera, Amaia

English

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Temporal Activity Detection in Untrimmed Videoswith Recurrent Neural NetworksAlberto MontesETSETB TelecomBCNUniversitat Politècnica de CatalunyaBarcelona, Catalonia/Spainmalberto@student.ethz.chAmaia SalvadorImage Processing GroupUniversitat Politècnica de CatalunyaBarcelona, Catalonia/Spainamaia.salvador@upc.eduSantiago PascualTALP Research CenterUniversitat Politècnica de CatalunyaBarcelona, Catalonia/Spainsantiago.pascual@tsc.upc.eduXavier Giro-i-NietoImage Processing GroupUniversitat Politècnica de CatalunyaBarcelona, Catalonia/Spainxavier.giro@upc.eduAbstractThis work proposes a simple pipeline to classify and temporally localize activitiesin untrimmed videos. Our system uses features from a 3D Convolutional NeuralNetwork (C3D) as input to train a a recurrent neural network (RNN) that learnsto classify video clips of 16 frames. After clip prediction, we post-process theoutput of the RNN to assign a single activity label to each video, and determine thetemporal boundaries of the activity within the video. We show how our system canachieve competitive results in both tasks with a simple architecture. We evaluateour method in the ActivityNet Challenge 2016, achieving a 0.5874 mAP anda 0.2237 mAP in the classification and detection tasks, respectively. Our codeand models are publicly available at at: https://github.com/imatge-upc/activitynet-2016-cvprw1 IntroductionRecognizing activities in videos has become a hot topic over the last years due to the continuousincrease of video capturing devices and online repositories. This large amount of data requires anautomatic indexing to be accessed after capture. The recent advances in video coding, storage andcomputational resources have boosted research in the field towards new and more efficient solutionsfor organizing and retrieving video content.Impressive progress has been reported in the recent literature for video classification [7–10], whichrequires to assign a label for the input video. While this task is already challenging, it has typicallybeen explored with videos to be trimmed beforehand. However, a video classification system shouldbe able to recognize activities in untrimmed videos, and find the temporal segments in which theyappear. This second challenge has been recently proposed in the ActivityNet Challenge 2016 [3], inwhich participants are asked to both provide a single activity for each video, as well as the temporalsegment where the activity happened in the video. In order to face both these challenges at the sametime, we propose a simple pipeline composed of a 3D-CNN that exploits spatial and short temporalcorrelations, followed by a recurrent neural network which exploits long temporal correlations.1st NIPS Workshop on Large Scale Computer Vision Systems (LSCVS 2016), Barcelona, Catalonia/Spain.arXiv:1608.08128v2  [cs.CV]  11 Dec 20162 Related workSeveral works in the literature have used 2D-CNNs to exploit the spatial correlations between framesof a video by combining their outputs using different strategies [4, 12, 1]. Others have tried using theoptical flow as an additional input to the 2D-CNNN [9], which provides information of the temporalcorrelations.Later on, 3D-CNNs were proposed in [7] (known as C3D), which were able to exploit short temporalcorrelations between frames and have demonstrated to work remarkably well for video classifica-tion [7, 8]. C3D have also been used for temporal detection in [6], where multi-stage C3D architectureis used to classify video segment proposals.For temporal activity detection, recent works have proposed the usage of Long Short-Term Memoryunits (LSTM) [5]. LSTMs are a type of RNNs that are able to better exploit long and short temporalcorrelations in sequences, which makes them suitable for video applications. LSTMs have been usedalongside CNNs for video classification [10] and activity localization in videos [11].In this paper, we combine the capabilities of both 3D-CNNs and RNNs into a single framework. Thisway, we design a simple network that takes a sequence of video features from the C3D model [7] asinput to a RNN and is able to classify each one of them into an activity category.3 Proposed ArchitectureWe use the C3D model proposed in [7] to extract features for all videos in the database. We split thevideos in 16-frames clips and resize them to 171×128 to fit the input of the C3D model. Featuresfrom the second fully connected layer (fc6) are extracted for each video clip.3.1 ArchitectureWe design a network that processes a sequence of C3D-f6 features from a video, and returns asequence of class probabilities for each 16-frames clip. We use LSTM layers, trained with dropoutwith probability p = 0.5 and a fully connected layer with a softmax activation. Figure 1 showsthe proposed architecture. Different configurations of the number of LSTM layers N and thenumber of cells c have been tested and are compared in Section 4.3. Our proposed system hasthe following architecture: input(4096) - dropout(0.5) - N × lstm(c) - dropout(.5)- softmax(K+1) where K is the number of activity classes at the dataset.Figure 1: Global architecture of the proposed pipeline.23.2 Post-ProcessingGiven a video, the prediction of our model is sequence of class probabilities for each 16-frame videoclip. This output is post-processed to predict the activity class and temporally localize it. First, toobtain the activity prediction for the whole video, we compute the average of the class probabilitiesover all video clips in the video. We consider the class with maximum predicted probability as thepredicted class.To obtain the temporal localization of the predicted activity class, we first apply a mean filter ofk samples to the predicted sequence to smooth the values through time (see Equation 1). Then,the probability of activity (vs no activity) is predicted for each 16-frames clip, being the activityprobability the sum of all probabilities of activity classes, and the no activity probability, the oneassigned to the background class. Finally, only those clips with an activity probability over a thresholdγ are kept and labeled with the previously predicted class. Notice that, for each video, all predictedtemporal detections are activity class.p˜i(x) =12ki+k∑j=i−kpi(x) (1)4 Experiments4.1 DatasetFor all our experiments we use the dataset provided in the ActivityNet Challenge 2016 [3]. Thisdataset contains 640 hours of video and 64 million frames. The ActivityNet dataset is composedof untrimmed videos, providing temporal annotations for the given ground truth class labels. Thedataset is split in 50% for training, 25% for validation and 25% for testing.4.2 TrainingWe train the network described in Section 3.1 with the negative log likelihood loss, assigning a lowerweight to background samples to deal with dataset imbalance (see Equation 2).L(p, q) = −∑xα(x)p(x) log(q(x)), where α(x) ={ρ, x = background instance1, otherwise(2)where q is the predicted probability distribution and p the ground truth probability distribution. In ourexperiments, we set ρ = 0.3.The network was trained for 100 epochs, with a batch size of 256, where each sample in the minibatchis a sequence of 20 16-frame video clips. We use RMSprop [2] with a learning rate set to 10−5.4.3 ResultsWe evaluate our models using the metrics proposed in ActivityNet Challenge. For video classification,we use mean average precision (mAP) and Hit@3. For temporal localization, a prediction is markedcorrect only when it has the correct category and has IoU with ground truth instance larger than 0.5,and mAP is used to evaluate the performance over the entire dataset.Architecture mAP Hit@33 x 1024-LSTM 0.5635 0.74372 x 512-LSTM 0.5492 0.73641 x 512-LSTM 0.5938 0.7576Table 1: Results for classification task com-paring different architectures.γ k = 0 k = 5 k = 100.2 0.20732 0.22513 0.221360.3 0.19854 0.22077 0.221000.5 0.19035 0.21937 0.21302Table 2: mAP with an IoU threshold of 0.5comparing between values of k and γ on post-processing.3Table 1 shows the performance of different network architectures. We tested configurations withdifferent number of LSTM layers and different number of cells. These results indicate that all thenetworks presented high learning capacity over the data, but some over-fitting was observed with thedeeper architectures, obtaining the best results with a single layer of 512-LSTM cells.Fixing the architecture, we performed experiments for the temporal detection task using differentvalues of k and γ in the post-processing stage. Table 2 shows results for the temporal activitylocalization task, where the effect of a mean smoothing filter can be seen, improving the localizationperformance. Figures 2 and 3 show some examples of classification and temporal localizationprediction for some instances of the dataset.Figure 2: Examples of temporal activity localization predictions.Video ID: ArzhjEk4j_YGround Truth: Building sandcastlesPrediction:0.7896 Building sandcastles0.0073 Doing motocross0.0049 Beach soccerVideo ID: AimG8xzchfIActivity: CurlingPrediction:0.3843 Shoveling snow0.1181 Ice fishing0.0633 WaterskiingFigure 3: Examples of activity classification.5 ConclusionIn this paper we propose a simple pipeline for both classification and temporal localization of activitiesin videos. Our system achieves competitive results on both tasks. The sequence to sequence nature ofthe proposed network offers flexibility to extend it to face more challenging tasks in video processing,e.g. where more than a single activity is present in the video. Future work will address end toend training of the model (3D-CNN + RNN), to learn better feature representations suitable for thedataset.4References[1] Nicolas Ballas, Li Yao, Chris Pal, and Aaron Courville. Delving deeper into convolutional networks forlearning video representations. arXiv preprint arXiv:1511.06432, 2015.[2] Yann N Dauphin, Harm de Vries, Junyoung Chung, and Yoshua Bengio. Rmsprop and equilibrated adaptivelearning rates for non-convex optimization. arXiv preprint arXiv:1502.04390, 2015.[3] Bernard Ghanem Fabian Caba Heilbron, Victor Escorcia and Juan Carlos Niebles. Activitynet: A large-scale video benchmark for human activity understanding. In Proceedings of the IEEE Conference onComputer Vision and Pattern Recognition, pages 961–970, 2015.[4] Georgia Gkioxari, Ross Girshick, and Jitendra Malik. Contextual action recognition with r* cnn. InProceedings of the IEEE International Conference on Computer Vision, pages 1080–1088, 2015.[5] Sepp Hochreiter and Jürgen Schmidhuber. Long short-term memory. Neural computation, 9(8):1735–1780,1997.[6] Zheng Shou, Dongang Wang, and Shih-Fu Chang. Temporal action localization in untrimmed videos viamulti-stage cnns. In CVPR, 2016.[7] Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, and Manohar Paluri. Learning spatiotemporalfeatures with 3d convolutional networks. arXiv preprint arXiv:1412.0767, 2014.[8] Du Tran, Lubomir Bourdev, Rob Fergus, Lorenzo Torresani, and Manohar Paluri. Deep end2endvoxel2voxel prediction. arXiv preprint arXiv:1511.06681, 2015.[9] Limin Wang, Yuanjun Xiong, Zhe Wang, and Yu Qiao. Towards good practices for very deep two-streamconvnets. arXiv preprint arXiv:1507.02159, 2015.[10] Li Yao, Atousa Torabi, Kyunghyun Cho, Nicolas Ballas, Christopher Pal, Hugo Larochelle, and AaronCourville. Describing videos by exploiting temporal structure. In Proceedings of the IEEE InternationalConference on Computer Vision, pages 4507–4515, 2015.[11] Serena Yeung, Olga Russakovsky, Ning Jin, Mykhaylo Andriluka, Greg Mori, and Li Fei-Fei. Everymoment counts: Dense detailed labeling of actions in complex videos. arXiv preprint arXiv:1507.05738,2015.[12] Serena Yeung, Olga Russakovsky, Greg Mori, and Li Fei-Fei. End-to-end learning of action detection fromframe glimpses in videos. arXiv preprint arXiv:1511.06984, 2015.5

Temporal activity detection in untrimmed videos with recurrent neural networks

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Temporal activity detection in untrimmed videos with recurrent neural networks

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