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[Page no. 14] "14 M. Halawa et al. controls the impact of the action-based contrastive loss into the final objective function. As shown in Fig. 3, we obtain the best results on all benchmark datasets by setting β to 0.75. 5 Discussion and Conclusions We presented a contrastive framework for learning behavior-aware pedestrian trajectory representations. Our proposed framework consists of an action-based contrastive loss, and a novel trajectory sampling technique from a learned dis- tribution of a C-VAE model. The proposed framework significantly improves the performance of trajectory prediction models on three different first-person view benchmarks. Our evaluation results provide evidence that including pedes- trian behavior information, in the form of action or activity class in this case, is beneficial for trajectory prediction. Moreover, our results also confirm that our action-based contrastive loss, in conjunction with our sampling strategy, is superior to alternative approaches that also utilize action class information. This work comes with a number of strengths. First, we ensure our proposed contrastive loss can be easily integrated with commonly used trajectory pre- diction models. Second, our proposed sampling strategy utilizes readily learned distributions by generative models, such as CVAEs, and it avoids designing data- specific heuristics. This allows for wider range of applications, such as on ani- mal trajectory data. Finally, contrastive learning in general, and our proposed action-based framework, in particular, allow for enhancing the quantities of un- derrepresented action classes in the data. This line of work may help address the shortage of necessary edge-cases in training datasets, which may be encountered in real-world scenarios. This work also comes with a limitation. While effective, our proposed action- based contrastive framework requires pedestrian action labels during the training phase only. However, this requirement is mitigated by our new trajectory sam- pling technique from CVAE, which does not require action labels"

[Page no. 2] "robots, modeling the motion information based on past trajectories only is a reasonable way to solve the task. However, in this work, we argue that when other agents in the scene are pedestrians, then limiting the information used for prediction to past trajectories is not sufficient. In those cases additional information about pedestrian behavior (e.g. action) plays an important role for predicting their future trajectory. For example, the future trajectories of a pedestrian who is walking while texting on arXiv:2207.08664v1 [cs.CV] 18 Jul 2022  2 M. Halawa et al. a phone could be different from a pedestrian carrying an object or pushing a baby stroller even if they have the same previous observed trajectories, and the same end goal. In this work, we study the influence of observed pedestrians’ actions on their predicted trajectories. We propose a novel contrastive learning loss called Action- based Contrastive Loss. This novel loss is employed as a regularizer to the main trajectory prediction loss. The action-based contrastive loss encourages the tra- jectory embeddings of agents performing the same action (called positive sam- ples) to come closer to each other in the feature space, and the embeddings of trajectories observed while performing different actions (called negative sam- ples) far away from each other. For instance, the representations of trajectories of walking pedestrians are encouraged to become closer in the feature space, but farther from the representations of trajectories of pedestrians riding bikes or standing, as illustrated in Fig. 1. Contrastive learning losses, including ours (action-based contrastive loss), utilize a mechanism called negative sampling/mining, which aims to choose the samples that are deemed different and therefore their corresponding features are driven farther in the embedding space. In our case, the negatives are trajectories of pedestrians that have different actions. Commonly used negative sampling techniques include"

[Page no. 3] "choosing all other samples from the same mini-batch [5] or from a fixed-size memory bank [14]. Nevertheless, while these mechanisms prove effective on natural imaging datasets, we find they do not provide similarly high gains on trajectory datasets. We conjecture that this is due to the higher variation in visual data compared to trajectory data, and most importantly, to the larger sizes of imaging datasets, e.g. Imagenet [8] contains 1.6M images compared to PIE [31] that contains 738,970 trajectory samples. This results in limited numbers of negative samples, an issue that becomes more evident when conditioning samples by class information, e.g. action or behavior. Few works attempt to address this issue via designing special heuristics for negative mining [23,38,13]. Alternatively, in this work, we propose to utilize the data distribution learned by a Conditional Variational Auto-Encoder (CVAE) [35]. This avoids designing special heuristics for negative mining. While this form of sampling may be utilized to create negative samples only, we employ it to create both positive and negative samples. This is possible due to the different definition of our contrastive loss compared to the traditional Noise Contrastive Estimation loss (NCE loss); the notion of positive/negative in our case is tied to the different classes of action in the data. As explained above, the samples that belong to the same action class are positives from the point of view of this class, and other samples are negative. Contributions. Our main contributions in this paper are as follows: – A novel contrastive loss, called action-based contrastive loss, which provides the model with additional information about the action of an agent by guid- ing the development of the embedding space for trajectories during learning.  Action-based Contrastive Learning for Trajectory Prediction 3 – A novel sampling/mining technique that utilizes the latent trajectory distri-"

[Page no. 5] "contrastive loss. Contrastive trajectory prediction: Contrastive learning is a representation learning approach, first proposed by [29]. This approach encourages similar high-dimensional input vectors to be mapped closely to each other in a lower- dimensional embedding manifold, and the dissimilar ones are mapped far away from each other. Contrastive learning has been applied in several unsupervised [29,15,5,11,41,4,14,6,10,17] and supervised [18] representation learning methods. Recently, only few works applied contrastive learning to trajectory prediction in a multi-agent setting. The flexibility of defining a contrastive loss by using positive and negative samples addresses the shortage problem in critical and challenging scenarios in training datasets. Such rare scenarios are necessary for the model, as the agent could face these in the real-world. Makansi et al. [25]  Action-based Contrastive Learning for Trajectory Prediction 5 utilize this idea by separating the hard and critical samples in the feature space that do not satisfy some certain favorable criterion far away from the positive easy samples. Liu et al. [23] proposed a social sampling strategy that relies on augmenting negative samples with prior knowledge about undesired scenarios in the multi-agent setting. Both methods use the contrastive loss as a weighted combination to the future trajectory forecasting loss, which may be the mean squared error (MSE) or negative log-likelihood (NLL). Our method follows this family of algorithms, and uses a novel action-based contrastive loss to add con- text information about pedestrian actions to the trajectory prediction model. Supervised contrastive loss: Khosla et al. [18] proposed a supervised con- trastive loss that is a generalization of the Triplet loss [16]. In this supervised contrastive loss for each anchor there are more than one positive sample, in addition to many negative samples. There are two major differences between our proposed action-based contrastive loss and the supervised contrastive loss used in"

[Page no. 5] "[18]. First, they employ the supervised contrastive loss to replace the cross-entropy loss for training the image classifier using image labels. However, we utilize the contrastive loss to regularize the trajectory prediction loss, which may be MSE or NLL. Second, due to the differences between the nature of datasets we use in this paper and the image data used in [18], it is simpler to ex- tract many positive and negative samples from a large dataset, such as ImageNet [8]. However, in first-person view trajectory prediction datasets, the number of pedestrians with same actions is limited, therefore we address this with a novel sampling process from a CVAE trained to predict trajectories based on observ- ing a short past trajectory. This CVAE predictive model ensures consistency between observed and predicted trajectories. Thus, it allows sampling additional positive and negative samples that belong to specific actions. Using this novel sampling technique avoids designing hard negative mining techniques, which use heuristics, as in [36,19] for domain adaptation. 3 Methodology In this section, we present our method for the task of pedestrian trajectory prediction, that focuses on integrating contextual information such as actions for more reliable future predictions. We address this by employing an action- based contrastive loss that enhances the trajectory prediction model with action information. 3.1 Problem Formulation For each pedestrian we have an observed past trajectory St = [s1, ..., st−1, st] at time t, and we predict a future trajectory Yt = [yt+1, yt+2, ..., yT ], where s and y are bounding box coordinates for the observed and predicted trajectories, respectively. T is the maximum predicted trajectory time length in the future. In addition, we also have for each trajectory the action class information a, where the set of available actions a ∈ {a1, a2, ..} may vary"

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