Introduction

There is no denying the fact that a human-like understanding of video surveillance by recognizing object or activity poses a significant challenge for autonomous systems in urban areas. In smart cities or environments, autonomous systems and people exist together in shared public spaces. Technology advancements have enabled the machines/systems to understand or recognize human actions in videos, but accurate and efficient human action recognition is a potential conundrum for the researchers in the field of computer vision. This area is still open for further research to develop systems that can be used productively in a stable and reliable manner. In areas where autonomous systems have to interact with people, it is very important that they have information about what people are exactly doing in their immediate environment. This is especially true if a direct interaction with the human being is to take place. Since human actions are highly dynamic, it is not only important to predict the actions correctly but also in real-time. Action recognition and prediction are two major tasks in computer vision and action recognition. It is a primary task that recognizes human simple actions based on the complete actions in a video. It plays a key role in many domains and applications including intelligent visual surveillance [1, 2], video retrieval, gaming [3], home behavior analysis, entertainment, autonomous driving vehicle, human–robot interaction, health care and ambient assisted living [4, 5]. Human action recognition in video includes various tasks like human detection, pose estimation, human tracking, and analysis. Basically action recognition can be classified at different levels of abstraction depending on the complexity [6] of visual information. It varies from simple actions such as concept/gesture activity, interaction with object/human to a complex action as a group activity.

Related work

Human action recognition is a crucial and challenging area owing to the accomplishment of the same action in a plethora of ways, even by the same individual. Besides, due to camera view point, occlusions, noise, complex dynamic background, long-distance and low-quality videos, action recognition still remains a challenging problem. A typical action recognition framework consists of two components: action representation and action classification [7]. In action representation, an action video is converted into a series of feature vectors and in action classification; an action label is inferred from the vector [8]. However, in deep networks, the above two steps are merged into a single end-to-end trainable framework by enhancing the classification performance. Action representation is the first and foremost important problem in action recognition, because human actions differ in videos due to motion speed, camera view, pose variation, etc. The major challenges in action recognition arise due to large appearance and pose variations. So, to overcome these challenges, an action video is converted into a feature vector by extracting representative and discriminative information of human actions by minimizing the variations. Action representation approaches are broadly categorized in two ways: holistic features and local features. Holistic representations capture rich and expressive motion information of humans for action recognition, but these methods is sensitive to noise and cluttered background. Bobick et al. [9] presented Motion Energy Image (MEI) and Motion History Image (MHI) framework to encode dynamic human motion into a single image. However, these methods are sensitive to viewpoint changes. Weinland et al. [10] propounded the 3D motion history volume (MHV) to overcome the viewpoint dependency in the final action representation. Local representations overcome the problems in holistic representations by identifying local regions containing salient motion information. Local features depict local motion of a human in space–time regions which are more informative than surrounding areas. Thus, features are extracted from these regions after detection. There are many successful methods such as space–time interest points [11] and motion trajectory [12], which are based on local representations, and these techniques are robust to translation and appearance variation. Bregonzio et al. [13] used Gabor filters to detect spatial–temporal interest points (STIP) and further points was computed using Hessian matrix. Several descriptors were proposed later including 3D SIFT, HOG3D, and local trinary patterns. Laptev et al. [14] worked on local neighborhood to compute optical flow features and aggregated in histograms, known as histograms of optical flow (HOF). Further, HOF features were combined with histogram of oriented gradients (HOG) features to show complex human activities. The author has identified and used various visual features for automatic sign recognition applications [15, 16].

Action classifiers learn from training samples to determine the accurate class boundaries for various action classes after action representations. There are other classifiers for human interactions and RGB-D videos. Ryoo and Aggarwal [17] used body part tracker to extract human interactions in videos by applying context free grammar to model spatial and temporal relationships between individuals. A human detector was adopted to recognize human interaction by capturing spatio-temporal context of a group of people and spatio-temporal distribution of individuals in videos. This method performed well on collective actions and it was further extended to a hierarchical representation which models the atomic action, interaction, and collective action all together [18]. Due to advancement of Kinect sensor, action recognition from RGB-D videos has received a lot of attention as it provides an additional depth channel compared to conventional RGB videos [19]. Many techniques such as histogram of oriented 4D normals and depth spatio-temporal interest points were proposed using depth data for action recognition task.

In recent years, many deep learning techniques have been popular due to their ability to do powerful feature learning for action recognition from massive labeled datasets [20]. There are two major variables in developing deep networks for action recognition, one is convolution operation and the other is temporal modeling. A 3D CNN is a multi-frame architecture which captures temporal dynamics in very less amount of time and can create hierarchical representations of spatio-temporal data [21]. Multi-stream network architecture contains two-stream network, a spatial ConvNet and temporal ConvNet, where the first stream learns actions from still images and the second one performs recognition based on optical flow field. This network does the fusion of outputs generated from two streams by their respective Softmax function, but it is not appropriate for gathering information over a long period of time [22]. The major drawback in the two-stream approach is that they do not allow interactions between the two streams and this is important for learning spatio-temporal features in videos. Hybrid networks contain a recurrent layer (such as LSTM) on the top of the CNN to aggregate temporal information to get the benefits of both CNNs and LSTMs [23, 24]. It has shown very good performance in capturing spatial motion patterns, temporal orderings, and long-range dependencies. In this paper, we focus on exploring the deep structure You Only Look Once (YOLO) object detection model for action recognition. YOLOv3 is a popular object detection model in real time and used to reduce the pre-training cost, increase the speed without affecting the performance of action recognition. Yan et al. [25] has introduced YOLOv3 framework for human object interaction recognition and results are achieved 93% accuracy on their own multitasking dataset.

Object detection method

YOLOv3

YOLOv3 object detector is became a popular detector due to its outstanding speed (45 frames per second). It is based on Darknet architecture (darknet-53), which has 53 layers stacked on top, giving 106 fully convolution architecture for object detection. YOLO takes the entire input image (608 × 608) in a single instance and divides it into an S × S grid (19 × 19). Then it predicts center of location (x and y axis), size (width and height) and probability of the object in each grid. For each grid cell, it estimates B bounding boxes along with its confidence score. The confidence score indicates that the probability of the box carries an activity (Threshold: 50%) and the accuracy of the box. There are 5 prediction parameters in each bounding box along with the activity classes such as Pc, x, y, h, w, c1, c1,c3,…..c80. Figure 1 shows the attributes of bounding box, where tx, ty, tw, th are the box co-ordinates, P0 is the objectness score and P1, P2, P3,…..Pc are the class scores, while B is the number of bounding boxes.

Fig. 1
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The attributes of bounding box in YOLOv3

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Table 1 shows sample bounding box values computed by YOLO for each image per class. The first value indicates the class number followed by values for x, y, h, w. The range of x and y values are always between (0, 0) to (1, 0), but the height and width may be more than 1, if the object fits into more than one grids in the image frame.

Table 1 Sample bounding box values for different image classes
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The network structure of YOLOv3 for object detection is shown in Fig. 2. This structure has three detection layers to detect different objects such as small, medium and large. It performs prediction in three scales by precisely down-sampling the dimensions of the input image by 32, 16, and 8, respectively. The first detection is made after 82nd layer and after the 81 layers, the image is down sampled by the network with a stride of 32. Then the second detection is made at the 94 layer and the third detection is made at the 106th layer.

Fig. 2
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Network architecture of YOLOv3

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CiRA-core

This experiment has implemented using CiRA-core which is based on Robot Operating System (ROS). It is a robot system integration platform developed by Tongloy et al. supported by Thailand Research Fund (TRF) and the National Science and Technology Development Agency (NSTDA). It facilitates the users in manipulating industrial robots by using deep learning. CiRA-core provides a number of modules such as DeepTrain, DeepDetect, DeepCrop, DeepLandmark, etc. In this paper, DeepTrain module is used for feature annotation (labeling the actions in the image) and training deep neural networks. Then DeepDetect module is used to recognize the human actions in the image using deep learning weight file. For identifying human object interaction, DeepCrop module is used to crop the objects from the images, and then DeepLandmark module to detect the human-interactions with objects more accurately.

Proposed MAMO recognition visual system

Multi activity multi object recognition system (MAMO) is proposed by using YOLOv3 standard framework. Figure 3 portrays the block diagram of our proposed architecture. It is implemented in two modules such as DeepTrain and DeepDetect modules.

Fig. 3
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Schematic diagram of proposed architecture

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DeepTrain

The input video sequences are converted into image frames, and then it is loaded into the DeepTrain module. In the feature annotation step, these images are manually labeled with the action classes for various activities, and then the ground truth file (e.g. activity.gt) is prepared. Then using auto gen feature, all the images are rotated with an angle of 45 degree variation from − 180 to + 180 degree and the bounding box values for each image are computed. The image frames are trained using batch size = 64 and sub division = 16 to generate the object files (obj.data, obj.names), configuration file for train (train.cfg) and test (test.cfg) and weight file (train.weights). Figure 4 shows DeepTrain module on image frames of AVA dataset.

Fig. 4
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Demonstration of DeepTrain module on image frames on AVA dataset

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DeepDetect

The weight and configuration files are loaded to DeepDetect to check the action label and confidence score for each action in that image. Assumption of the average loss value to be 0.05 for preparing the weight file and 50% confidence score as threshold for action detection are considered.

The accuracy of human–object interaction has improved by using DeepCrop module to crop the labeled action from the image frame and then trained using DeepTrain module. Further, DeepDetect module is used to detect the interactions from the cropped images. It is observed that, there is a drastic improvement in confidence score for the human–object interactions from the cropped images. Cropped images enhance action detection more accurately than the entire image frame due to variation in background, brightness, clutter, and noise present in the image. Sometimes, certain human interactions with small objects (e.g. “cutting with a knife”) can’t be detected more accurately from an entire image, so, DeepCrop and DeepLandmark modules are required to improve the action recognition. To detect the small objects, DeepLandmark module has used. It is a 2-stage YOLO working on two different weight files, one on uncropped image weight file and cropped image weight file, to improve the detection of the human interactions more accurately. The Fig. 5 shows the block diagram of our work on DeepCrop and DeepLandmark.

Fig. 5
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The DeepCrop and DeepLandmark model for human-object interaction

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Data description

The video data used for this experiment includes various levels of objects and interactions by considering standard datasets like KTH-6 activities, UCF-11 or YouTube Action Dataset-11 activities, AVA Dataset-80 classes in 3 categories and Collective Action Dataset-6 activities.

KTH dataset

This dataset comprises 6 types of human actions such as walking, running, boxing, jogging, waving and clapping given in Table 2.

Table 2 Action class categories in KTH dataset with abbreviation
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These are performed multiple times in 4 distinct scenarios. All the video sequences were captured with a still camera (25 fps frame rate) and over homogeneous background. Figure 6 shows human action images from KTH dataset.

Fig. 6
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Human actions in KTH dataset

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UCF dataset

This is an unconstrained dataset which contains 11 types of action categories such as basketball shooting, biking/cycling, diving, golf swinging, horseback riding, soccer juggling, swinging, tennis swinging, trampoline jumping, volleyball spiking, and walking with a dog are given in Table 3.

Table 3 Action class categories in YouTube action (UCF-11) dataset with abbreviation
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There are numerous issues including variations in camera motion, object appearance as well as pose, illumination, and cluttered background. All videos are categorized into 25 groups with more than 4 action clips in it and for our experiment; we have taken samples from all action clips. Figure 7 shows human action images from YouTube action dataset [26].

Fig. 7
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Human actions in YouTube action dataset

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AVA dataset

This dataset is taken from 15 to 30 min videos of 430 different popular movies, with a sampling frequency of 1 Hz and a total of 900 key frames for each movie. It contains 80 annotated atomic visual actions, where each action is localized in space and time. All the 80 classes of this dataset are given in Table 4.

Table 4 Action class categories in AVA action dataset with abbreviation
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It is divided into 3 categories: simple actions (14 classes), human–object interaction (49 classes), human-to-human interaction (17 classes). In each frame, a person is localized using a bounding box and the corresponding label is attached. This dataset contains classes related to atomic actions such as bending, crawling, sitting, jumping, etc. as well as interactions classes with objects and humans such as climbing, cooking, cutting, kissing, hugging, fighting, etc. Figure 8 shows action images from different categories.

Fig. 8
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Human activities from AVA datasets: a human atomic movements (crawl, bend, sit, stand); b human–object interactions (answer phone, driving, row boat, work on a computer) and c human–human interactions (hand shaking, kiss, hand clap, lift a person)

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Collective action dataset

This is a group action dataset which is given by the University of Michigan and contains 6 distinct collective activities: crossing, waiting, talking, queuing, dancing, and jogging given in Table 5.

Table 5 Action class categories in collective action dataset with abbreviation
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These are performed by people from 44 short video sequences. These videos are recorded by consumer hand-held camera from different view-points. In all video sequences, every 10th frame is annotated with image location of the person, activity id and pose direction. Figure 9 shows group activity images from collective action dataset.

Fig. 9
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Collective human actions (crossing, queuing, talking, waiting)

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Results and discussions

In this experiment, the model has trained to 10,000 iterations with an average loss of 0.05 with a batch size of 64 and 16 subdivisions. We have taken different challenging datasets (KTH, UCF-11, AVA, collective action) to train our model and predict the accuracy of human activities. The model performance has assessed through Intersect over union (IoU) measure. We have chosen the weights file with the highest IoU and then used this weight file for detection (train_10000.weights). The experiments were performed on Intel(R) Core(TM) i5-8600 CPU@ 3.10 GHz with GPU(GeForce GTX 1070 Ti), Graphics card RAM size of 8 GB, and on Ubuntu 16.04LTS (64 bit) operating system. Table 6 shows some of the training parameters used by DeepTrain model on sample datasets.

Table 6 Training parameters generated by DeepTrain model
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Our primary evaluation metric is prediction accuracy on different interactions (actions) taken from AVA image datasets. Figure 10a–c shows the accuracy at each class along with linearity among classes of 3 categories of actions such as human atomic actions, human–object interactions and human-to-human interaction.

Fig. 10
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Prediction accuracy of 3 categories of interactions using YOLO v3 on various classes in AVA dataset; a human atomic actions; b human–object interactions and c human-to-human interaction

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In AVA dataset, it is observed that various classes like sit (SI), sleep (SL), bend (BE) from human atomic actions, answer phone (AP), hit (HI) from human–object interactions and give or serve (GS), grab (GR) from human-to-human interactions have more mismatched classification done by model. Figure 11 shows the confusion matrix for 14 category subset by using deepTrain model of action based classification. This is used to calculate precision, recall, specificity, F1-score, and overall accuracy measures of the model. Figure 12 shows automatic visual predictions of human actions with confidence score from different datasets.

Fig. 11
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Confusion matrix for 14 category subset by using deepTrain model of action based classification

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Fig. 12
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Automatic visual predictions of human actions with confidence score from different datasets, a KTH; b UCF-11; c AVA

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The results obtained are compared quantitatively with the state-of-the-art techniques proposed so far for the datasets used in this paper and it is presented in Table 7. Based on the comparison, it is observed that YOLOv3 shows the best results for action recognition on various challenging datasets.

Table 7 Comparison of accuracy on different datasets (KTH, UCF-11, AVA, collective action)
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Conclusion

In this paper, we proffer a real-time model for human action recognition in videos by employing the avant-garde object detector YOLOv3. It is observed that YOLOv3 detects the activities more accurately by taking even small number of frames with high confidence score. And this model performs action recognition very well irrespective of occlusion, cluttered background, variation in viewpoint, inter and intra-class similarities present the image frames. We focused on detecting simple to complex human actions using gray scale to RGB image frames taken from video sequences. DeepLandmark model more accurately detects human activities performed with small objects such as “smoking a cigar”, “cutting with a knife”, “fishing” etc. It is also observed that YOLOv3 take more time for training on large datasets. The aim of this paper is to focus on complete human behavior analysis through human actions and we found YOLOv3 to be the more accurate human action detector so far.